Precise Information Control in Long-Form Text Generation
Authors: Jacqueline He
Howard Yen
Margaret Li
Shuyue Stella Li
Zhiyuan Zeng
Weijia Shi
Yulia Tsvetkov
Danqi Chen
Pang Wei Koh
Luke Zettlemoyer
Venue: NeurIPS 2025 poster ()
Keywords: faithful hallucination
faithful text generation
long-form generation evaluation
controllable language models
strict grounded generation
Primary area: applications
TL;DR: We introduce Precise Information Control (PIC), a framework that reframes faithful hallucination in long-form generation as a claim-level control problem, and present a benchmark, initial models, and practical demonstrations.

Abstract: A central challenge in language models (LMs) is faithfulness hallucination: the generation of information unsubstantiated by input context. To study this problem, we propose Precise Information Control (PIC), a new task formulation that requires models to generate long-form outputs grounded in a provided set of short self-contained statements, without adding any unsupported ones. PIC includes a full setting that tests a model’s ability to include exactly all input claims, and a partial setting that requires the model to selectively incorporate only relevant claims. We present PIC-Bench, a benchmark of eight long-form generation tasks (e.g., summarization, biography generation) adapted to the PIC setting, where LMs are supplied with well-formed, verifiable input claims. Our evaluation of a range of open and proprietary LMs on PIC-Bench reveals that, surprisingly, state-of-the-art LMs still hallucinate against user-provided input in over 70% of generations. To alleviate this lack of faithfulness, we introduce a post-training framework that uses a weakly supervised preference data construction method to train an 8B PIC-LM with stronger PIC ability—improving from 69.1% to 91.0% F1 in the full PIC setting. When integrated into end-to-end factual generation pipelines, PIC-LM improves exact match recall by 17.1% on ambiguous QA with retrieval, and factual precision by 30.5% on a birthplace fact-checking task, underscoring the potential of precisely grounded generation.

Precise Information Control in Long-Form Text Generation
Jacqueline He Howard Yen Margaret Li Shuyue Stella Li Zhiyuan Zeng
Weijia Shi Yulia Tsvetkov Danqi Chen Pang Wei % Luke Zettlemoyer"
"Paul G. Allen School of Computing Science Engineering, University of Washington "Princeton Language and Intelligence (PLI), Princeton University "Allen Institute for Al
[jyyh, pangwei, 1sz] @cs. washington. edu
Abstract
A central challenge in language models (LMs) is faithfulness hallucination: the generation information unsubstantiated by input context. To study this problem, propose Precise Information Control (PIC), new task formulation that requires models to generate long-form outputs grounded in a provided set of short self-contained statements, without adding any unsupported ones. PIC includes a full setting that tests a model's ability to include exactly all input claims, and a partial setting that requires the model selectively incorporate only relevant claims. We present PIC-Bench, benchmark of eight long-form generation tasks (e.g., summarization, biography generation) adapted to the PIC setting, where LMs are supplied with well-formed, verifiable input claims. Our evaluation of a range of open and proprietary LMs on PIC-Bench reveals that, surprisingly, state-of-the-arr LMs still hallucinate against user-pre scoviprovdee input in over 70% of generations. To alleviate this lack of faithfulness, we introduce cost-training framework that uses a weakly supervised preference data construction method to train an 8B PIC-LM with stronger PIC ability-improvee
from 69. % to 91.0% Fi the full PIC setting. When integrated into end-to-end factual generation pipelines, PIC-LM improves exact match recall by 17.1% on ambiguous QA with retrieval, and factual precision by 30.5% birthplace fact-checking task, underscoring the potential precisely grounded generation.
1 Introduction
Exact control over what content language models (LMs) should produce is an open challenge in long-form generation; L.Ss may miss key details, or generate superfluous or false claims. This is true not only for unconstrained tasks that depend on parametric knowledge, but even when the prompt states exactly what information the model ought to include. Omissions, distortions, and fabrications collectively constitute particular breakdown of information control, more generally referred as hallucinations
43
42
In language modeling
hallucinations are generations that cannot be verified by external real-worle knowledge, while faithfulness hallucinations contradict supplied input context knowledge 75
Faithfulness hallucination is problematic in instances where the LM must reliably transform or synthesize source text, such as in scientific literature synthesis [5), abstractive summarization (75) or arbitrary style transfer [97]. The elimination thereof is just as critical in
fast-moving or high-stake real-world applications in which updated, reliable in-context evidence must override the model's s stale parametric memory [14] Examples include gathering the latest scientific consensus on COVID-19, or generating customized medical guidance based on a patient's
Equual contribution.
39th Conference on Neural Information Processing Systems (NeurIPS 2025).

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Full PIC System Prompt
Verifiable Claims
Model Output
Device has known ove fhealingg issue.
Thanks reaching out. We're aware that this cevice may experience overneatng and, in cerren pases, battery seeling in some units. We have traced this issue to the ginggngggg port, and relessed fir fimwere update addresses this
Camplete the instruction incladie exactly all fo lolowing claims-no more, no fewer.
Overheating occurs mest frequentiy during gaming.
Some device units are known to heve nattery swelling g.
Device usage should be stapaed if the device feels extremely hot
update v2.3 fares the overheeting Issue.
Instruction
dlevice replacament available units
you may elgitte free replacement, Would you
Write sustomer support message about a defective dovice given the follewing updsles.
under 1 year ole.
more information?
Extract Verify
Recall 1/2
Model Output Claims
dlaim
Overbeeting is a known issue of thi device.
Complete the instruction using any subset the following claims that help your answer.
Covered slaim
Uncovered claim
Uncovered claim
Uncovered claim
Score
Some cevice units have experienced battery swelligg
Firmware update v2.4 was released to address this aigblle
This isuue has bean tracad to the cnarging port.
B. Covered cialm
unit leas
Partial PIC System Prompt
free reptacement
Score
Precise Information Control
Fuc Score (Fi) 6/11
Precision 3/S
Partial PIC Soore (Prec.) - 3/5
G.G.O.O
O O
Precise Information Control (PIC) evaluates faithfulness hallucination at the level of verifiable claims. It has two settings: full PIC requires including all input claims and no others, while partial PIC requires a subset of input claims (exact system prompts in Table TO).
Figure 1:
approved treatment list. Furthermore, even efforts to reduce factual hallucination may require faithful grounding on retrieved filtered context 77, making intrinsic control critical building block for trustworthy generation.
To this end, we propose Precise Information Control (PIC), a task formulation for assessing faithfulness hallucination in long-form generation. PIC raises a fundamental question: can LMs produce long- form responses that strictly grounded a set of explicit statements, without introducing any unsupported claims?
we frame this problem at the level of verifubble claims-short standalone information units that can be unambiguously validated [105). To support more flexible, user-defined control, we consider two task settings specified in natural language. Full PIC reflects scenarios that require complete information coverage (c.g., text rewriting), where the LM should incorporate all given input claims (evaluated with Fi). Mcanwhile, partial PIC applies when the context includes irrelevant or extrancous claims, requiring selective grounding (e.g., summarization). Therefore, in this case, the LM ought choose only relevant subset (evaluated with precision) for its response. As an example, Fig. [ illlstraaess for a customer- message about defective device, following updated guidelines. Full PIC requires the LMM to integrate all six claims into its answer, while under partial PIC, the LM may choose any helpful subset. Both PIC modes require precise control: every output claim must be corroborated by input claims.
Instead of forming binary judgments of faithfulness hallucination 78
To evaluate PIC across long-form generation, we introduce PIC-Bench, a examlee benchmark covering six full and two partial PIC task settings. In PIC-Bench evaluation, the verification search space is restricted to input context. Therefore, unlike with factual hallucination [IO, 77 22 47 [13] [17]. the complete climination of faithfulness hallucination ought to be tractable and well-defined. Nevertheless, we find that PIC is far from solved on existing state-of-the-ar LMs. In particular, perfect PIC (i.e., zero faithfulness hallucination) remains out of reach. The proportion of outputs with perfect PIC does not exceed 30% (measured by Fi) for full PIC tasks, and 70% (measured by precision) for partial PIC tasks. This means that even frontier LLMs still produce faithfulness hallucinations in over 70% of full PIC outputs. Further, leading open LMs consistently underperform proprietary LMs by over 8%, indicating headroom for improvement.
We then develop a two-stage post-training framework to improve PIC ability, using a weakly-supervised reward signal to efficiently construct PIC-formatted preference data. Specifically, we tune Llama 3.1 8B Instruct 33 with this framework to produce PIC-LM, a highly controllable model: its generations reliably adhere to input claims, while reducing unsubstantiated statements. Relative to Llama 3.1 8B Instruct on PIC-Bench, PIC-LM raises average full PIC from 69.1% 1. to 91.0% in F1, and average partial PIC from 73.6% to 93.3% in precision, outperforming all open baselines and closing the performance gap to frontier LLMs. Nevertheless, the elimination of faithfulness hallucination in long-form generation remains elusive, underscoring the challenging nature of this problem.
Finally, we demonstrate the practical utility of PIC-LM in downstream applications. While faithfulness and factuality hallucination are often addressed separately in prior work [10 42), our results suggest

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that robust faithful grounding may help reduce factual errors. Embedding our models into generation pipelines, where input either substantiated with context or iteratively refined, yields more factually accurate and reliable final outputs. In come from retrieved documents, PIC-LM improves standard exact match from 52.5% to 61.5% on ASQA, an ambiguous long-form QA task. In a automatically generated and verified, PIC-LM raises factual accuracy on a birthplace factoid task (from 65, 65.9% to 86.0% in factual precision) and QAMParl [2] (from 13.5% to 22.6% in F) @5)..
generation setting 7 where claims
pipeline [20] where claims are
2 Task Formulation
2.1 Problem Definition
we consider PIC at the level of verifiable claims: short statements that describe a standalone event state, and are evaluated with respect some knowledge source (in our case, input claims). Decomposing outputs into individual claims common long-form
Verifiable claims.
Following 70
[05]
120 Claims can be represented various granularities, for example, at the sentence level 45 72 or atomic level (i.c., imparting the most minimal unit of information) (77 [20). Sentence- claims may mix supported and unsupported information (77), while strictly atomic claims often lack the context needed for unambiguous verification. Further, not every claim is inherently provable, e.g., metaphors, analogies, other complex linguistic constructs.
evaluation technique 777 72 105
Verifiable claims strike balance between sentence and atomic granularity: they are concise enough to isolate specific assertions, but with sufficient context for unambiguous verification (see Fig. [ for examples). We obtain verifiable claims from long-form outputs using an automatic, LLM-based claim extractor from Song et al. [105].
PIC settings. A PIC task input entails an instruction I and a set of verifiable claims C = C1,..... Cu- Given (I, C), an LM f generates long-form response 8(I, C), from which set of verifiable claims C" c...., é'm can be extracted. For comprehensiveness, we define two modes based on user intent, each with distinct constraints.
In the full PIC setting, the LM must incorporate every given claim into its response, neither adding omitting any information. Specifically, each claim should appear at least once in the generation, and no statements may appear in the response without an input claim to support it. Full PIC task examples may include reliable style transfer or open generation (e.g.. "Write a short biography Elena Ferrante", given a highly curated set of claims).
In the partial PIC setting, the LM may incorporate only relevant subset of the context claims, and introducing new claims remains disallowed. For tasks such as summarization or QA with retrieval, not all input claims should be reproduced-only the most salient information is desirable (e.g.. "Summarize this given passage on economic policies Seattle, Washington", or "Write a short biography Elena Ferrante", given set of input claims retrieved from Wikipedia entries). However, the generated output should remain strictly grounded in provided context. Therefore, the partial setting requires that C' C C' (no additions are allowed, but omissions are permissible).
To meaningfully assess a PIC task, we require that the input claim set C' be well-formed, satisfying: (1) non-emptiness: C * 9, (2) no duplicates: Vc,, Cj E C, i + = o cj. (3) no contradictions: fc,, Cy E C such that e; contradicts cj. and (4) task relevance: should contain enough well-formed claims to adequately fulfill the instruction I, though not necessarily exhaustive.
The last criterion (task relevance) ensures that the task remains simple and tractable for the LM by avoiding adversarial claims that may obscure the intended instruction. In practice, our evaluation of PIC tasks does not directly account for instruction followwng ability. The orthogonal relationship between instructions and claims can be separately evaluated through dedicated cestruction-followwee metric (which we explore in (5.2) that requires the LM simultancously satisfy the provided claims and adhere to requirements defined by its PIC setting.
2.2 Task Construction
We can recast any long-form generation task from a standard instruction-tuning dataset Drr into our PIC format. Let Dir consist of samples (I, p, y). where I is the task instruction, p is optional
3

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context (i.e., a source document for a summarization task), and is a long-form gold response For full PIC, we assume these tasks do not contain p. so we extract C' from y. In partial PIC, we extract C directly from p. In either case, we end up with (I, C), where C' is well-formed.
2.3 Task Evaluation
We automatically evaluate the PIC ability of generated responses by first decomposing output into verifiable claims using a claim extractor [105], and then employing an LLM-based claim verifier support(:;" -) [105] that checks for semantic equivalence. Both claim extraction and verification exhibit strong agreement with human judgments (Appendix [.4). Formally, for any candidate claim c and claim set S, let
True, if c is semantically equivalent to some S, False,
support(e,
otherwise.
In other words, it follows that
x
support (z, C)H
Recall .C) ( E C support C')}
Precision Precision(C''O. CC
|C]
|C|
Intuitively, the precision is the proportion of claims in the response that are supported by the context claims, while the recall is the proportion of context claims that are supported in the response. Given both, we can also calculate the Fi.
Recall that the full setting requires that C' = C (no omissions or additions). Hence, we evaluate with Fi, which penalizes omissions (recall). The partial setting requires that C' S C' (no additions are allowed, but omissions are allowed), so we only consider precision. For both settings, we also report the proportion of samples with zero faithful hallucination-ddnntee as the proportions of perfect Fi and prefect precision, respectively
3 PIC-Bench
3.1 Evaluation Tasks
PIC is applicable to any long-form generation task. We design PIC-Bench, a benchmark of six full and partial PIC tasks We choose eight long-form generation datasets from prior work [8] 77 32 105 42 and re-Trame their evaluation to our problem formulation. Note that PIC-Bench assesses only how well the model's 's responses adhere to PIC constraints when completing a task, instead of downstream task performance (which can be assessed orthogonally). Table shows PIC-Bench task information (additional details in Appendix B.I.
Full PIC tasks. For the full PIC setting, choose tasks where comprehensive coverage of the input verifiable claims is integral to high-quality response. ENTITY BIOspIc [77 a biography generation task with high number of claims. PopBios 332 is short -oorraphy-generation dataset for popular entities, from which we derive two tasks: PorBios-P Ppic and PorBios-CF -CPic. PorBros-Prre PPic asks the model to generate biography of a well-known entity in line with the LM's parametric knowledge, while PopBros- CFric is a counterfactual variant which the parametric entity is systematically replaced with another well-known entity. AsKHISTORIANSYIC and ELI5pIC (105) are Reddit-sourced long-form QA datasets on historical topics or simplified explanations to complex topics, respectively. EXPERTQAPiE 72 is a long- forg-form QA task of expert-curated, domain-specific questions. In full PIC, all claims are extracted from the long-form gold response y.
Partial PIC tasks. For the partial PIC setting, we identify tasks where selective grounding is appropriate- -that is, where only relevant subset of context claims should appear in the output.
We assume that Dyr follows the same format as the Alpaca [108] dataset, in which not every sample contains context p. so the model input is either (a, p) if provided, or just æ. faithfulness rather than fluency completeness: every verifiable output claim must be
'PIC focuses anchored to the provided context.
"Some tasks (e.g., biography generation) can be either full or partial PIC, depending user intent and claim coverage. For simplicity, each PIC-Bench task is assigned exactly one PIC setting.

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Table 1: PIC-Bench dataset statistics and examples. For the partial PIC setting, we omit the full context for brevity. N is the number of task samples, and C is the average number of input claims.
Task Name
PTC Type
N
C
Example Instruction
ENTIYBiOSHI
Foll
183
50.5
Generate factual biography about Suthida.
PorBios-Pri
Foll
111
20.1
Give me a biography on Erwin Schrödinger. the scientist who discovered Quantum Mechanics, Schrödinger's Cat Thought Experiment.
Pop
III
20.1
Give biography on
who discovered Quantum Mechanics, the following question(s]: why it's
Wilde,
Schrodinger' Cat Thought Experiment
EL
Foll
146
12.5
87-octane gasoline US but
it's almost always 95-octamc in Europe? In the original Star Wars: A New Hope, Obi Wan Kenobi instructs R2-D2 connect the Imperial netwods to guin access to the whole system. Did the concept of an interconnectedd computer netwerk exist in 1977?
Full
158
19.2
ExteetQAp FACTSpk:
152
between modern and contemponrry architecture? Explain the benefits of using mobile technology to impeove healthcare management in both hi-income and low-income countries. [Contest p) Summarize the following text in around 20-25 words. |Context p)
question(s):
Partial
150
63.5
XSUMsic
Purtial
200
30.6
FACTSPIC 442 is factual grounding task, in which LMs must base their answers on long-context documents. XSUMPIC [8]] is abstractive summarization task of BBC news articles. XSUM originally required one-sentence summarization, but we extend it to a longer-form setting by imposing higher minimum word count. In partial PIC, claims are sourced from the supplied context p.
3.2 Baselines
We benchmark a range of sfate-of-the-art, instruction-tune tuned LMs. Among open-weight LMs, we consider standard instruction tuned LMs along the 8B and 70B parameter ranges: Llama 3.1 8B Instruct and 3.3 70B Instruct [33], Tulu 3 8B and 70B [52], Ministral 8B Instruct [80], and Hermes 3 8B and 70B [TT. We also consider 32B open-
reasoning models: Qwen 32B [00].
QwQ 32B 110. and R R1-Qwen-32BB (distilled from DeepSeek-R1) [18]. These models are capable of step-by-step reasoning based on thinking control tokens; we report results using the thinking mode, which consistently yields better performance (see Appendix B.6 for non-thinking mode results).
Finally, we consider frontier LLMs as approximate skylines: GPT-4o 86 and Claude-3.5 Sonnet [4].
PIC-LM: A Post-Training Approach
We propose PIC-LM, an 8B instruction- contuuttio-followinng language model with high PIC ability. PIC-LM follows conventional, two-stage post- post-training process: supervised fine-tuning (SFT) followed by mortth-normalized DPO 52]. Key to our approach is a novel weakly supervised preference data construction method that optimizes for PIC, while preserving isstucttoonfoolooiing capabilities.
4.1
Supervised Fine-Tuning (SFT)
To provide the LM with a strong initialization for PIC, we conduct SFT on a diverse mix of high-quality, long-form instruction-tuning data. This mix includes two multi-task datasets-No Robots [93 and FLAN
118 -as well as a CNN news summarization [10I. biography generation [77). and long-form
QA tasks 42 For PIC-Bench evaluation, the biography and QA datasets are in-distribution (ID), whereas the other datasets are out-of-domain (OOD), allowing us to assess performance in both settings. Following the procedure in $ each sample is converted the PIC format, yielding the triple (I, C, v) We then filter the data sample pool by only retaining samples with high PIC scores. We conduct SFT from Llama 3.1 8B Instruct [33], producing OSFT.
4.2 Preference Tuning
Preference data construction. Recall that in our SFT dataset, each sample consists of an instruction I, a set of original context claims Corigs and an original gold response Yorig- We construct perturbed variant of each Corig by randomly dropping subset of the claims, resulting in a reduced context Gpentuhh C Corig. I, Cpenunt are then passed to QSFT, which already has learned PIC ability, to obtain the response Ypertub- Thus, for each sample, we have (I, Conig Cpertub Yorig 3pertub).
5

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Our preference dataset should consist of tuples C ,y), in which I is the instruction, C' is the appropriate context (chosen to match the preferred response), and yy and y denote the chosen and rejected responses, respectively. Each sample leads two valid constructions of (I, C, y
(I. Gorig: Yorig, Upermuh))
(I, C, .8 -
(Z, Opsnub Ypentb, Yori
Preference data sampling strategy. One naive approach to choosing y is to sample each construction with equal probability, which suffices if the goal is only to optimize for PIC ability, Since 'lperuub is often shorter than Yorig, random sampling may also mitigate length bias [8 92]. However, this strategy does not account for instruction-follovii ability: if Cpertur drops too many essential claims, 1/petuhh may fail to adequately answer I, degrading instruction alignment. Conversely, not all dropped claims are equally important- -Upertub may better satisfy Z by being more concise, focused, or stylistically aligned. This motivates a strategy that adaptively selects between yoorig and 3ppettub based on overall instruction compatibility.
Thus, taking inspiration from prior reward strategies [4], propose weakly supervised data sampling strategy to construct (I, C, y uy*y"" yy) in a way that balances PIC and instruction following skills. Using a competent instruction foliionnn- reference model 0, we first define a normalized log-probability drop score follows:
(C) log po (Yorig
I)
log po (Upenuth I)
Normalized Log- Pogg-arbability Drop -a
L
which is the normalized difference in per-token log probability of generating the original response versus the perturbed response, computed over the last tokens of each sequence (wherein L is fixed, e.g., 20 tokens) for efficient computation and to alleviate length bias. We normalize the difference into the interval (0, 1) via the logistic function (a(2) = Ites
For each data sample, if the normalized probability drop score exceeds some hyperparameter E [0, 1]. then select (I, C, yt, y = (I, Corig; Morig: Vpertuhh) ) and (I, Cperturb Y/pertuh; Morig) otherwise. We assume that the per-token log probability is a reasonable proxy signal for instruction-followiin ability, meaning that the probability of 0 generating given response diminishes if the response cannot sufficiently fulfill the instruction without necessary context. A larger probability drop indicates that the perturbed response significantly worse than the original response. Empirically, we select different r values for full and partial settings, and observe that this yields better instruction-followe performance than random sampling
Training. We train fSTT on our constructed preference data using length-normalized direct preference optimization, which is simply DPO with tog-probaiilities normalized for length [91] [2] (6).. In Appendix C.4, we present ablations that (i) skip the SFT step, i.e. initialize directly from Llama 3.1 8B Instruct, and (ii) experiment with alternative loss functions.
5
Main Results
5.1 PIC-Bench Results
Table 2 and Table show PIC-Bench results in full and partial PIC settings, respectively, and Appendix B.2.4 shows corresponding 95% bootstrapped confidence intervals. We highlight few salient trends.
The complete elimination of faithful hallucination is challenging, even under ideal conditions. By design, PIC-Bench tasks are an ideal instantiation of PIC with straightforward goal: to construct responses that ground on only werl-formed verifiable claims, without introducing additional information. Despite this intentional lack of complexity, even frontier LLMs fail to achieve PIC scores close to 100%. The average proportion of perfect PIC scores is even lower, not exceeding 30% in the full and 70% in the partial setting.
We ablate several values of and show high LLM-as- correlation with GPT-4.1 in Appendix C.4

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Table 2: PIC-Bench results (full setting). Metries include Fi, the proportion of perfect Fi (Perf.), reported per-task and as an average. Best values bold. All reasoning LMs (denoted with f) are evaluated with thinking mode enabled. For all metrics, the higher the better.
ENTBrIc Fi Perf.
PorB-Pric
PorB-CFric
ASKHrIc Fi Perf.
ELI5Mic F Perf.
ExPQApic Fi Perf.
Avg. Pj Perf.
Pi Perf.
Fi
Perf.
Open-weight LMs (8B)
LLAMA 3.1 8B INST.
74.4
0,6
83,5
2.7
23.7
0.0
81.9
5.1
77.8
8.9
73.5
5.3
69.1
3.7
TULU 3 8B
79.6
1.6
87.8
2.7
51.3
0.0
80.4
3.2
81.8
10.3
80.7
11.8
76.9
4.9
MINISTRAL 8B INST.
78.6
0,6
89.0
9.0
63,5
0.0
76.8
5.1
78.1
11.0
77.6
15.1
77.3
6.8
HERMES 3 8B
74.9
0.6
84.9
6.3
44.6
0.0
72.9
2.5
75.9
4.1
74.1
7.9
71.2
3.6
Oeinhw LMs (32B)
QWEN 32B
88.2
18.9
48.2
0.9
77.5
7.0
67.7
6.9
63.6
69.9
QwQ 32B"
86.7
1.6
87.6
2.7
64.1
1.8
79.6
3.8
80.0
6.9
80.9
12.5
70.3
7.6
RI- RI-QWEN-32BB
82.3
1.1
87.5
9.0
58.8
0.9
81.1
4.4
82.2
10.3
80.0
13.2
78.6
6.5
Open-weight LMs (70B)
LLAMA 3.3 70B INST.
89.1
93.4
17.1
45.5
0.0
91.2
12.7
90.3
19.9
85.6
27.0
82.5
13.1
TULU 3 70B
84.8
7.1
89.2
23.4
52.0
0.0
83.7
15.8
82.5
19.2
73.4
17.8
77.6
13.8
HERMES 3 70B
81.4
1.6
89.4
12.6
56.6
0.0
81.6
5.1
79.7
9.6
79.8
14.5
78.1
7.2
Proprietary LMs
CLAUDE 3.5 GPT-40
88.8
93.1
1.0
0.0
83.7
85.8
21.1
69.4
16.3
91.9
94.0
27.0
74.6
0.9
96.1
37.3
93.9
47.3
92.7
47.4
90.5
27.9
Ours: PIC-LM (8B)
from LLAMA 3.1 8B INST.
PIC-LMsFT ONLY
86.7
87.2
7.2
71.7
1.8
86.6
82.1
19.2
21.7
81.9
11.7
PIC-LM
94.1
96.0
36.0
81.0
3.6
95.1
32.3
94.1
52.7
85.6
34,9
91.0
28.0
Table 3: PIC-Bench results (partial setting). Met- ries include precision (Prec.), the proportion of perfect precision (Perf.), reported per-task and as average. Best values in bold. All reasoning LMs (denoted with a f) are evaluated with think- ing mode enabled. For all metrics, the higher the better.
Proprictary models mostly lead weight baselines. GPT-4o consistently outperforms the open-weight baselines by meaningful margin on average task performance. Claude 3.5 underper- forms against most baselines the full PIC setting (primarily due to very low F1 PopBros-CFruch but beats all open-weight models in the partial setting. Within each model family, larger models tend to achieve higher PIC scores. Different open- weight model families exhibit substantially varied performance, indicating that PIC ability isn't t solely determined by model size. Despite their larger size, 32B reasoning models achieve PIC scores compa- rable to 8B non-reasoning models, suggesting that explicit reasoning capabilities alone do not drive substantial gains on PIC tasks.
FACTSpic
XSUMpic
Avg Prec.
Avg Perf.
Prec. Perf.
Prec. Perf.
Open-weight LMs (8B)
LLAMa 3.1 INSt.
70.0
19.3
77.2
54.0)
73.6
36.7
TULU 3 8B
75.3
18.7
76.9
50.5
76.1
34.6
MINISTRAL 8B INSt.
75.4
30.0
82.7
63.0
79_1
46.5
HERMES
74.1
27.3
8122
42.0
77.7
Open-weight LMS (32R)
QWEN 3 32B"
64.5
17.3
78.5
46.0
71.5
31.7
QwQ 32B'
70.3
12.7
86.9
58.5
78.6
35.6
LMs struggle in the counterfactual PIC regime. PorBros-CFric is the most challenging task, with perfect-F, scores in the single digits. It serves as stress test, swapping biographical claims with counterfactual entities that directly conflict with a model's parametric knowledge. This simulates sce- narios such as updating outdated facts or enforcing user-specific overrides, where strict instruction- following is desirable even when it contradicts internal priors.
R. RJI-WeN-32BB
71.1
26.7
53.5 79.6
75.3
40.1
Open-welght LMS (70B)
LLAMA 3.3 70B Isst.
79.4
32.0
69.3
67.0
74.3
Tulu 70B HERMES 3 70B
67.7
14.7
85.5
60.0
76.7
37.3
78.0
25.3
87.0
62.5
82.5
43.9
Proprictary LMS
CLAUDE 3.5 GPT-40
82.0
53.3
90.4
77.0
86.2 65.2
87.4
93.0
Ours: PIC-LM (8B)
from LLAMA
3.1
8B
PIC-LMuTOOI
81.2
46.0
94.0
69.5
87.6
57.8
PIC-LM
89.8
49.3
96.8
49.3
93.3
52.9
Among wpinhweight baselines, Llama 3.1 8B Instruct performs worst with an average 23.7%. while among closed models, Claude 3.5 achieves only 1%, as it frequently abstains from producing
7

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factually incorrect outputs Although structurally similar to PorBios-PPer one of the easiest PIC tasks, the conflicting entities PopBios-CE create extreme knowledge collisions, consistent with Wu et al. [21 8 finding that LMs fail when input knowledge diverges from internal priors.
PIC-LM shows substantial improvements. Our 8B PIC-LM outperforms all open-weight baselines across both evaluation settings. In the full evaluation setting. PIC-LM achieves an average Fi of 91.0% and perfect-Fi proportion of 28.0%; compared to the strongest open-weight baseline, Llama 3.3 70B Instruct, at 82.5% and 13.1%% respectively. In the partial evaluation setting, PIC-LM reaches an average precision of 93.3% and a perfect-precision proportion of 52.9%, surpassing the best open-weight baseline, Hermes 3 70B, at 82.5% and 43.9%, respectively.
Even the ablation model PIC-LMsFT Only whhch is trained only with SFT on PIC-formatted data and without further preference apiimizatinn-sussaanniiin highlights that simply fine-tuning on PIC-formatted instructions provides significant boost to PIC performance. Starting from the weakest open-weight baseline, PIC-LM nearly closes the gap to GPT-4o, surpassing it in both average F1 (91.0% vs. 90.5%) and average precision (93.3% vs. 90.2%). For the proportion of perfect scores, PIC-LM performs competitively with GPT-4o in the full evaluation setting (28.0% vs. 27.9%) but trails GPT-4o and Claude 3.5 Sonnet in the partial setting (52.9% vs. 67.7% and 65.2%, respectively).
outperforms Llama 3.1 8B Instruct. This
Nevertheless, PIC-LM outperforms all open-weight LMs by considerable margin across every metric, demonstrating that a simple post-training strategy on an 8B open model can achieve near-state- of-the-art PIC performance without the need for closed-source models or larger parameter budgets.
5.2 PIC-LM Maintains Instruction Abilities
An ideal controllable LM ought to generate responses that can answer the instruction in a useful manner, while fully grounding source context when required 42]. Orthogonal to PIC, we explore how well PIC-LM can follow instructions on each PIC task. Following Asai et al. (5) we employ Prometheus 2 49 an open-source evaluator LM that scores long-form outputs based on custom instruction-followiin rubric from 1 to (defined in Table
A degenerate case of PIC happens when the LM learns to hack the metric by reproducing the input claims C verbatim, leading to an unsatisfactory response with perfect PIC score. As a sanity check, we introduce CONCATCLAIMS, lower- bound setting that simply concatenates input claims into a response. Despite perfect PIC, CONCATCLAIMS should perform poorly on Prometheus as claim concatenation ignores task-specific requirements (e.g., word length, style).
Table 4 shows Prometheus results (mean + SE) for each PIC task. CONCATCLAIMS scores poorly (averaging 2.01) as it fails to satisfy task instructions. In contrast, Llama 3.1 8B Instruct is significantly instruction-ture
better at requirements. Compared to Llama 3.1 8B Instruct, PIC-LMsFT Only shows slight decline across most tasks except for gains on the counterfactual task PorB-CFpic and XSUMPIC. which brings its average performance to 3.87. PIC-LM mostly regains its
(3.75);
model, it competently follows task
ability except on XSUMPIc- We observe qualitatively that it experiences some difficulty in following the strict word constraint. Nevertheless, PIC-LM has the highest Prometheus average at 3.92. Altogether, these results indicate that PIC-LM generally preserves the source model's 's instruction -oslloiiinn-ollowiing ability.
Table 4: Instruction-following evaluations using Prometheus, reported the task average with standard error. CONCATCLAIMS a degenerate lower bound (Low). Best values in bold.
ENtB.Ric
PorB-Pric PorB--FFic
AssHyrc
ELI5pi
ExeQApi
FACTSei
XSUMPIc
Avg.
CONCATCLAMMA (Low)
2.26 m
2.77 12 2.44..oo
2.31 + m 2.01
2.21 2.
1.97 m
1.76 a
1.71
1.06 0
2.01
LLAMA 3.1 8B INST.
4.22
4.07 2.00
4.14
4.11
3.71
3.28
3.75
Ours: PIC-LM from LLAMA 3.1 8B INST.
PIC-L Owly
3.79 m. m 3.84 m.
4.10
3.99 2.
m
4.01
3.93
3.87
3.57
3.87
PIC-LM
4.18 Am
3.79 u.
4.21 00
4.09 +
4.03 n
4.01 .0
3.23 "
3.92
'Claude's RLHF alignment to "helpful, harmless, and honest" principles likely causes this abstention 3
8

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Practical Applications of PIC-LM
One major assumption to PIC-Bench is its reliance on well-formed input claims. Identifying appropriate claims in real-world scenarios, however, poses challenges: (1) claims may not be well-formed, and (2) users may not know which claims to include in the desired response (e.g.. for fact-finding tasks). We bridge the divide between our controlled benchmark and practical application by demonstrating that PIC-LM, when situated in multi-componte long-form QA tasks. First, with retrieval augmmnttd generation (RAG), PIC-LM can identify relevant claims from retrieved context to generate more accurate answers ($6.1). Second, in a velf-verification pipeline, swapping in PIC-LM the final generation step boosts end-task factual accuracy (...).
systems, improves factuality on
Baselines. First, as lower-bound baseline, we compare against Llama 3.1 8B Instruct without context (No Context (Low)). We also evaluate zero-shot prompting baselines with Llama 3.1 8B Instruct, Tulu 8B, Ministral 8B Instruct, and Hermes 3 8B, and two context-aware baselines that foster greater context reliance. The first is CAD (context-aware decoding) [02] decoding technique that factors out the LM's prior knowledge to emphasize attention toward input context; we apply it to Llama 3.1 8B Instruct. The second is SelfCite 8B [14), strong attributed LM that generates responses with context attributions (or citations), and is post-trained using a supervisdd reward that leverages context ablation. Since do not consider attribution quality in this work, strip out citation markers before evaluation.
6.1 Retrieval-Augmennee Generation
In atrieval-auggent generation (RAG). the input query is supplemented with relevant external
Recent work [67] 32 74 121 122 indicates that LMs fail to effectively use retrieved context when it conflicts with their internal knowledge, even when the correct answer is in the context. Unlike the strict input conditions imposed by PIC-Bench, retrieved context may contain duplicate, irrelevant, noisy claims. Can PIC-LM generalize such instances? We test on the ASQA [066] dataset, which comprises 948 long-form QA pairs. In ASQA, each question is under-specified and requires multiple short-form answers to fully disambiguate (e.g.. "Who was the ruler of France 1830?" is ambiguous and has multiple correct answers).
texts to guide the LM's generation 37 54 104
Following Gao et al. [27], we re- port exact match recall (EM), taking the top-5 retrieved passages from Wikipedia, decomposed to verifiable claims, as input context. We report a standard setting that involves all sam- ples, and an oracle setting with only samples where the retrieved context has at least one gold answer. [ shows results. Aside from Mistral 8B Inst., all context-based setups outper- form the no-context baseline (30.9% EM). PIC-LM achieves a statistically significant boost both regimes. Un- der the standard regime, it scores 61.5% 2.1 versus 56.2% + 2.1 for CAD, the strongest baseline.
Table 5: RAG performance on ASQA on standard (948 samples) and oracle (885 samples) regimes, reported as average EM + 95% bootstrapped CI. Best values bold.
Setting
Standard EM Oracle EM
No CONTEXT (Low)
30.9 2.2
N/A
LLAMA 3.1 8B INST.
52.5 121
56.2 2.0
TULU 3 8B
42.7 2.0
45.6 2.0
MINISTRAL 8B INST.
31.8+14
34.0
HERMES 8B
52.8 2.0
56.4 = 2.0
CAD (on LLAMA 3.1 8B INST.)
56.2 +
60.1 2.0
SELFCItE 8B
52.2 1 2.1
52.5 2.1
PIC-LM 8B
61.5 + 2.1
65.9 2.0
6.2 Self-Verification Pipeline
PIC-LM remains effective even in settings where relevant external claims are not provided. Drawing inspiration from ooaai--f-verificatti [20] and self- ceff-consittecc sampling II 115), we describe how PIC-LM can improve end-task factuality in a pipeline system that auto aito-generates and verifies claims, without any dependency on knowledge augmentation (e.g.. RAG or search tools). This approach leverages the observation that LMs typically perform verification more reliably than generation [246]] particularly for factual assessment (35). By exploiting this asymmetry, we can effectively self-correct problematic behaviors such factual hallucination
1 89 20 46).

---

Table 6: Average performance (with 95% bootstrapped Cls) on the BIRTHPLACE and QAMPARI tasks. The No CONTEXT setting serves as a lower bound. Best values in bold.
BIRTHPLACE
QAMPARI
Setting
Factual Prec.
Prec.
Rec.@5
F,@5
No CONTEXT (Low)
19.4
6.1
1.
6.5
LLAMA 3.1 8B INST.
65.9
11.++1
21.4+20
13.5+13
TULu 3 8B
80.3 4.0
16.8
2..
18.4
MINISTRAL 8B
83.7 4.1
27.2=2a
14.0
15.
HERMES 3 8B
53.4 + 4.2
9.9+
20.3
12.1+112
CAD (on LLAMA 3.1 8B INST.)
63.6 :
23.8
17.5
SELECITE 8B
69.4 1 4.5
17.7 11.5
121
18.6
LLAMA 3.3 70B INST.
84.7
14
26.8+22
+..7+14
PIC-LM
86.0 4.0
2..
25.6 2.22
22.6
1
Given an input instruction, the LM: (1) generates draft response; (2) formulates verification questions based on this draft output; (3) independently fact-checks each question with self-consistency sampling and majority voting to produce set of unique verified claims; and (4) produces a final response using only the verified claims that conform to the original instruction.
We deploy our pipeline on two downstream factuality tasks. First, following Dhuliawala et al. (20.. we evaluate on birthplace factoid task of the form: "Name some {occupation}s born in {location3", which we seed with 252 {occupation), {location} pairs. We report factual accuracy using average claim precision, in which the final response is decomposed into a set of verifiable claims, and each claim is fact-checked using Google Search via the Serper API [105]]]]] 120
an ODQA task for which answers consists of multiple entities that are spread across different sources (e.g.. "Which book had illustrations by Pauline Baynes?"). Following Gao al. [27], we report the precision, recall@A, and F1 @k on QAMParI, where k is the maximum number of correct answers used for recall calculation. We use Llama 3.3 70B Instruct as the generator LM for steps 1-3 on both tasks, which we find to be sufficiently large enough for accurate claim self-validation. At step 4, the final generation step where claims are transformed into long-form response, we can swap PIC-LM and different baseline approaches.
Second, we evaluate on QAMParl
Table shows chain-of-verificatioo results. On the birthplace verification task, PIC-LM outperforms all 8B baselines, and slightly surpasses the larger Llama 3.3 70B Instruct, demonstrating the efficacy of better PIC. On QAMParl, all context-supplied baselines perform similarly for Fi @5, with larger variances for precision (with Hermes 8B the lowest 9. 9.9%, and Ministral 8B the highest 27. 27.2%) and recall@5 (with Ministral 8B the lowest at 14.0, and Llama 3.3 70B the highest at 26.8%). PIC-LM 8B has the best trade-off between precision and recall@5, with the best Fi @5 at 22.6.
Discussion
Control is the keystone of reliable LM generation; it underpins user trust and system reliability, from everyday information-seekingg
[27 to high-stakes political, legal, and medical applications [7
58 (7]. In this work, we formulate faithfulness hallucination as claim-level control problem through the PIC, which breaks down long- generation into discrete verifiable claims. Our contribution three-pronged: we present a simple yet challenging benchmark, an initial training method and models, and motivating use cases.
The PIC paradigm confers two practical benefits. First, PIC ensures controllability by allocating the highest degree of faithfulness toward user-provided context over parametric knowledge. This design allows for the easy uptake of amended or updated information. The second advantage is interpretability: as all generated claims must be grounded in the input space, forming a closed feedback loop, the user can trace any output claim back to its source claim set, making verification precise and tractable. Future efforts may explore how to surface PIC behavior in earlier stages such as continual pre- pre-training either through the incorporation of learning cues via control sequences (28), the strategic
re-ordering of data samples [103), or procedural synthetic data generation [12 question is whether PIC-LM can accommodate more complex forms of control; e.g., adaptively switching between contextual and parametric knowledge. Finally, the practical utility of PIC can be expanded to more sophisticated agentic setups, such as tool use [99 or multi-LLM LLM collaboration (26]
71 125 One open
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Acknowledgments
We thank Rui Xin for helping with OpenAT API evaluations, as well as Noah Smith, Tianyu Gao, Luey He, Adithya Bhaskar, and the Princeton NLP Group for their helpful feedback. JH is supported by an NSF Graduate Research Fellowship. HY supported by the William A. Dippel '50 #55 Graduate Fellowship. SL is supported by the Meta AIM program, and ZZ is supported by an Amazon AI Ph.D. Fellowship. This research was developed with funding from the Defense Advanced Research Projects Agency's s (DARPA) SciFy program (Agreement No. HR001 12520300). The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. This material is based upon work supported by the Defense Advanced Research Projects Agency and the Air Force Research I Laboratory, contract number(s): FA8650-22-C--3166 Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of AFRL, or DARPA. Further, this work is also supported by the Singapore National Research Foundation and the National AI Group in the Singapore Ministry of Digital Development and Information under the AI Visiting Professorship Programme (award number AIVP-2024-001), as well as the A12050 program at Schmidt Sciences.
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Appendix
General Information
27
A.I Released artifacts
27
A.2 Related work
27
Limitations
28
A.4
Broader impacts
28
PIC-Bench Details
29
PIC-Bendl task details
29
B 2
PIC evaluation details
30
B.3
Inference settings
31
4
Human evaluations
32
B.5
Few-shot baseline results
34
B.6
Thinking mode ablations
34
B.7
Qualitative task examples
35
B.8
Llama scaling analysis
35
B.9
More PIC-Bench plots
37
PIC-LM Training Details
39
SFT data curation
39
Preference data creation
43
Training settings
49
PIC-LM ablations
49
C.5
Instruction following evaluation
larruction-foloowing
49
C.6
Domain shift generalization
50
Scaling PIC-LM training
50
D PIC Use Case Details
51
D.1
Implementation details
51
D.2 Additional experiments
52
26

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A General Information
A.1 Released artifacts
We release all artifacts (PIC-Bench, PIC-LM 8B models, and related data processing, training, and evaluation scripts) for reproducibility and future development:
Codebase
PIC-Bench Data
jacquelInnehe/pii--bnnch
PIC-LM 8B
jicquelinnehe/LLaaa33.111
PIC-LM 8B SFT
jaquellnehh//Lam-33-1111------ SFT
PIC-LM SFT Data
PIC-LM Preference Data
A.2
Related work
Hallucination in LMs. Hallucination is byproduct of the standard language modeling objective: LMs extract statistical patterns from training corpora, and learn to generate semantically fluent text that
is not necessarily accurate with respect to knowledge source 43 A profusion of recent scholarship
has focused on factuality and extrinsic hallucination, in particular, its root cause identification 22
47 30
detection [20, 73. evaluation [122] 77
[005] 56
120). and mitigation 44
53
9 13 6
Early faithfulness hallucination studies are largely task-specific, with efforts on abstractive
summarization
75 100 129
25 or knowledge-goounded dialogue generation 39 95 22
1
PIC-Bench evaluates faithfulness hallucination in long-form generation via claim decomposition,
in similar manner as recent long-form evaluation on factual hallucination [77 105| 120 Among faithfulness hallucination benchmarks, RoSE [66] considers generations at the level of atomic content units for summarization evaluation. FaithEval [78 operates on short-form QA, while we consider
range of long-form tasks. Closest to our work is FACTS Grounding 422 which we incorporate as partial task in PIC-Bench. Their evaluation relies on coarse, holistic assessment by prompting multiple judge LLMs, instead of a granular per-claim verification. Furthermore, PIC imposes stricter criteria via the full PIC setting by requiring LMs to ground all generated content in provided context, instead of using binary notion of faithfulness.
Evaluation aside, there are many contemporary efforts to alleviate LM hallucination via post-training. PIC-LM follows a conventional alignment procedure, similar recent factuality approaches 40 that use API-based automatic evaluators for preference data construction [77] 120 In contrast, PIC-LM leverages a more cost-effective reward signal and targets faithfulness hallucination. Several works improve context-faithfulie
63 1I
through other means, either via prompting [128] decoding modifications [102), or fine-tuning on counterfactual data [67 3 PIC-LM is not deliberately trained on counterfactual data, but nevertheless generalizes well to counterfactual evaluations.
RAG and Attributable Generation. PIC shares surface similarities with both RAG and LM attribution, task in which every statement in generated text must be ascribed to appropriate source references using inline citation markers [27 64 [26]. However, while the principal goal of RAG and LM attribution is to improve factuality, PIC is fundamentally an information control problem: ensuring that every output claim is traceable to contextual knowledge. This motivates a dedicated evaluation framework, benchmark, and training techniques.
While all three paradigms require grounded responses, we note several key differences. First, PIC enforces full coverage of all input claims, while RAG allows for information omission so long as final answers are correct. Second, attribution systems often work at the sentence or passage level, while PIC operates at the level of verifiable claims, enabling finer-grained detection of distortion, fabrication, and conflation. Lastly, while RAG and LM attribution primarily focus on QA, PIC is more univerally applicable to range of generative tasks.
We follow the newer and more granular taxonomy LLM hallucinations proposed by Huang et al. 41 which categorizes them into factuality and f faithfulnees hallucinations. Earlier work instead used the terms intrinsic and extrinsic, where intrinsic hallucinations directly contradict the source context, and extrinsic hallucinations cannot be verified using either the input context external knowledge base 45
27

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Controllability in LMs. Controllability is a fundamental concept that originates from control theory. It describes a system's ability to transition from any initial state to any final state through This is valuable property for LMs, as many user-centric applications require particular control over elicited output [177]. For example, a dialogue generation task may involve preferred levels of attributes such as emotion, formality, or toxicity. Early efforts to steer LM generation include training with discrete control codes [48, (95] to adjust style, content, and other task-specific behaviors, or modifying the output logit distribution at decoding time [124).
appropriate external inputs 85
55
In recent years, instruction fine-tuning has emerged as the de facto paradigm align LMs with user intents [79, 87 98 116, 118 68]. LMs must learn to implicitly map natural language instructions to desired output behaviors, rather than relying on special tokens to trigger actions. While instruction- tuning expands the expressivity of the constraint space, it also fosters greater ambiguity. LMs may struggle to follow commands, even in the presence of explicit cues. For example, Goyal et al. [2 find that supervised fine-tuning on instruction datasets diminish the LM's contextual reliance ability. They theoretically show that cenn--onexx--eeendeee instruction fine-tuning, Our work corroborates that this problem is data-driven: by fine-tuning on instruction pairs that require strict claim adherence, PIC-LM maintains strong contextual grounding. Further, DPO and related preference optimization algorithms more explicitly reinforce this behavior, ensuring a performance margin beyond what supervised fine-tuning can achieve alone.
data points eventually dominate loss gradients during
A.3 Limitations
consistent through-line is that user-provided knowledge should supersede the LM's inherent priors. Users ought to exercise full control in establishing the ground truth framework for LM generation, which is an extreme stance that may not be appropriate in every downstream scenario.
PIC imposes strong input assumptions for instance, that the set of input claims should be well- structured and exactly aligned with what constitutes an ideal response. The LM should anchor its response aser-given claims, even if this means forgoing extra correct details, or overriding the model's internal beliefs. Enforcing these constraints is necessary in fast-moving, high-stakes domains (e.g., scientific consensus on infectious diseases), where plausible but unverified information pose serious risks. That LMS cannot completely eliminate faithfulness hallucination on a simple and well-defined setting is significant. Further, we provide case studies illustrating how PIC imprrves factual accuracy when situated within a larger pipeline in which context fetched, verified, or filtered beforehand. Despite its limited standalone utility, PIC-LM demonstrates potential reliable component in modular systems.
Given the time and cost it takes to evaluate each generation sample via claim decomposition, our evaluation sets are individually quite small (each under 250 samples). This is par for the course: our testbed sizes are comparable to those used in long-form factuality literature (77 63 [05] [13]. Unlike prior work, our task selection extends beyond QA; it thorough, but by no means exhaustive. In line with Jacovi et al. [42], we do not consider multi-step logic, intensive reasoning, or creative generation.
A.4 Broader impacts
Our work addresses foundational aspect of language models by enabling a stronger command over the claims that LMs produce. PIC-LM serves as a reliable vehicle for transforming text without introducing intrinsic hallucinations, and improves factual accuracy in downstream applications. This holds important ramifications as LMs are the workhorse behind modern generative search engines (4) Our work addresses foundational aspect of language models by enabling a stronger command over the claims that LMs produce. PIC-LM serves as a reliable vehicle for transforming text without introducing faithful hallucinations, and improves factual accuracy in downstream applications. This holds important ramifications as LMs are the workhorse behind modern generative search engines (4
However, greater generation control may also raise the likelihood of misuse. PIC-LM is more liable to emitting counterfactual, toxic, or harmful content when presented with certain contexts, bypassing abstention behaviors imbued during RLHF. While sub-optimal, we argue that this tradeoff should be contextualized. Uncontrolled toxic generation, arising from model hallucinations, can be harder to detect and filter, which may be arguably worse. Recent work shows that LM safety guardrails are often superficial and can be easily bypassed [90), and that, despite extensive safety alignment, conventional LMs may still generate adversarial outputs rooted in opaque model internals. In contrast,
28

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PIC-LM shifts the safety locus from the model's parameters to user inputs, making the source of information transparent. Doing so facilitates a more controlled and manageable environment where safeguards can be designed around input validation and user intent. Therefore, while PIC-LM may be the safest model in isolation, we see as promising building block for safery-aware systems where user-grounded control is critical.
B PIC-Bench Details
B.1 PIC-Bench task details
Task information. Table 7 shows PIC-Bench task information, and Fig. shows the claim count distributions for all tasks.
shows
Claim Count Distribution
Rattlyh.
Popti (Pam.)
Pop (CF)
Awkitiet.
32
Coust
E135
ExperTOA
FACTS
XSUM
32
Number of chaime
Figure 2: Claim count distribution histograms under the full PIC (orange) and the partial PIC (teal) setting. The red dotted line indicates the average number of claims. Note that PoPB. -PIC and PorB.-CF -CPic share the same distribution.
Dataset details. Many of these datasets are originally introduced for long-form factuality 777 72 105), dimension we do not measure. Rather, our PIC metric seeks ask: how faithfully can LMs partially or in full? Even under ideal conditions (i.e., when the claims constitute the gold response), we identify a meaningful faithfulness gap in existing LMs. We describe each dataset in greater detail below:
incorporate given information-eithee
Entity Bios consists of named entities from Min et al. 77 obtain biographical context information about each entity from relevant Wikipedia pages. For each given entity, the corresponding instruction is: "Generate factual biography about entity."
Popular Bios is a biography generation task of famous real-world entities across various domains, e.g., art, science, literature [32). In this dataset, each sample contains both parametric entity and a counterfactual entity (who is also a famous individual into biographical query, e.g., "Generate a biography electromagnetism and electrochemistry." " for which the LM must rely on contextual cues to describe specific biographical entity. For controlled study, consider both parametric (e.g.. "Michael Faraday") and counterfactual (e.g.. "Nelson Mandela") settings.
different field). Each entity is slotted (entity), the scientist who discovered
EL15 (formally, Explain Like I'm Five) consists of human- questions and responses sourced from the Reddit forum!] in which users ask for simple explanations to complex questions or topics. We use subsampled set from [105..
29

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Table Dataset information for PIC-Bench. Average claim length words (split by whitespace).
Dataset
Sample Count
Min. / Avg. / Max. Claims
Avg. Claim Len.
Data Provenance
ENtryBrossic
183
50.5 79
12.0
Human (Search)
PoPBiospic 32
7/ 77.20.17
10.7
Synthetic
FL 123
146
4/ 12.5 /40
10.6
Human (Reddit)
AsHHISTORIANSIC
125
158
6/ 19.2/49
12.1
Human (Reddit)
EXPERTQAPIC
72
152
3/ 13155//55
11.6
Human
FACTS GROUNDINGHE!
150
6/.3.5/ 167
13.1
Human
XSUMpc 81
200
2/300. 171
12.1
Human
AskHistorians consists of in-depth and comprehensive human- questions and responses from the Reddit forum about various historical topics. We use a subsampled set from [005]
ExpertQA consists of expert-curated and expert-verified long-form QA pairs across 32 fields; use a subsampled set [72..
FACTS Grounding is long-context benchmark that evaluates the language model's ability to generate text that accurate with respect to context documents of up 32K tokens
XSUM a summarization dataset that contains news articles from the BBC 81] It was originally introduced for extreme, 1-sentence summarization. In case, for each sample, convert the given article into a list of claims and provide the following instruction: "Summarize the following text, given list of claims, in around 20-25 words."
Where applicable (i.e., if the context extracted from a buman-authored gold response, such as from the Reddit tasks), perform some lightweight quality processing by retaining only samples with Prometheus score above 4.
B.2
PIC evaluation details
PIC evaluation consists of three parts: claim extraction, claim verification, and metric calculation.
B.2.1 Claim extraction and verification.
We adopt the claim extraction and verification procedures introduced by Song et al. [105] For claim extraction, we apply few-shot prompting to GPT-4o mini, employing a sliding window format (i.c.. each sentence is supplied with its previous and successive sentences as context), such that the extracted claims are self-contained (i.e., grammatically and semantically unambiguous). To prevent artificially inflated scores, claims are de-duplicated before verification.
For claim verification, prompt GPT-4o mini to determine whether each claim is supported by the original context, assigning one of two labels: supported or unsupported. One response claim may be supported by one or more context claims. In contrast to Song et al. [105], instead of verifying against a list of retrieved snippets from Google Search, we check against the list of input claims.
B.2.2 Metric calculation.
We consider precision for the partial PIC setting and F1 (formally, Fi @) for the full PIC setting. Both metrics computed following Wei et al. [120] and Song et al. [105]. which motivate below.
Assume set of claims provided by the input as C, and a generated response y. Let Syy) be the number of supported claims, and Nyy) be the number of unsupported claims from the response Then, can compute the factual precision of response Prec(y) S(w) metric in the partial PIC setting.
S(y)
which is the final
In the full PIC setting, we would like to compute Fi @ K, which balances the "supportedness" of the generated response with the coverage of verifiable claims in C.
reddiit.cmm// httte//ccmmm//Asiiisttii.aaas
30

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Wei et al. [120] note that measuring factual recall is challenging, as it is impossible in most cases to come up with an exhaustive set of all possible claims that should be included in an ideal long-form response. They propose setting a hyperparameter K, which is some number of supported claims for which a user cares about recall up to the K-th supported fact.
The case for PIC is simpler: we only care that the generation incorporates exactly all claims in C, instead of external corpus of real-world knowledge. Accordingly, we can K = [C]. Let [Cy] be the count of unique claims in C' that are also present in y. Then, the factual recall@K K of is
Rx (y) min( [Col K>> 1)-
F1 K combines the precision and recall
P-Proo(y) (x(() Pree (V) (x)
if S(v) 0, if Syy) 0.
F
POK-
0,
B.2.3 Choice of LLM backbone.
PIC evaluation relies on LLM API that charges per-token for claim extraction and verification. The total cost is dependent on several factors, such as the number of extracted claims (as each extracted claim is verified in an independent API call), and the context length of input and output. In our experience, a full evaluation run of an open LM on PIC-Bench costs less than $7 USD when using GPT-4o mini as the LLM backbone, which we believe is reasonable under an academic budget.
To show that the PIC evaluation pipeline relatively agnostic to different LLM backbones, we report the effect of additional LLM judges (GPT-411 and Claude 3.5 Sonnet) average PIC-Bench metrics in Table 8 PIC scores are largely judge-agnostic, with GPT-4.1 behaving slightly stricter than the other judges. Given that GPT-4.1 and Claude 3.5 Sonnet impose API costs 13x and 20x higher respectively, we retain GPT-4o mini.
Table 8: Ablation of different LLM Judges PIC-Bench. We report average F1 and precision (in %) for each setting.
Evaluated Model
LLM Judge
Llama 3.1 8B Inst.
PIC-LM 8B
GPT-4o
GPT-4o MINI
69.1 73.6
89.9 / 91.2
90.5 / 90.2
GPT-4.1
68.6 73.6
87.5 / 88.1
87.6 / 86.0
CLAUDE 3.5 SONNET
70.2 74.5
88.2 / 90.0
89.6 / 89.3
B.2.4 PIC-Bench evaluation reliability.
To quantify statistical precision, we report 95% confidence intervals estimated via bootstrap resampling with 1000 replicates (Table 9.. Across most settings, the confidence interval bands are relatively narrow and often within 2-4 percentage points, indicating that our PIC metrics are statistically stable under sample variability. For proprietary LMs, the intervals are especially tight (e.g., GPT-4o, EntityB.: 91.9, 93.35), reflecting high metric consistency.
In contrast, wider intervals appear in a small number of more challenging or lower-scoring tasks (e.g., CLAUDE 3.5, PopB. (CF): 5.77, 9.99), suggesting greater variability in claim extraction or verification in those settings. Nevertheless, the overall narrowness of the CIs across tasks and systems corroborates that PIC evaluation can provide precise and trustworthy estimates of PIC performance.
B.3 Inference settings
Generation hyper-parameters. We use the vLLM toolkit [5]] for fast inference on all open, non-API-based LMs. For the non-reasoning models, we generate using greedy decoding (temperature set to 0) and repetition penalty set to 1.2, as generation grows more deterministic at lower temperatures [35] 53]. For the 32B reasoning models, greedy decoding results in considerably degraded or repetitive output. Thus, follow prescribed best practices: enable the thinking
31

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Table 9: 95% confidence intervals (CT) across all tasks and models PIC-Bench. For each setting, we sample 1000 replicates with replacement from each evaluation set. All reasoning LMs (denoted with f) are evaluated with thinking mode enabled.
EntityB.
PopB. (Para) PopB. (CF)
AskHist.
EL15
ExpertQA
FACTS
XSUM
Open-weight LMs (8B)
LLAMA 3.1 8B INST.
72.72. 76.09
81.48, 85,39
20.20. 27.09
79.05. 84.33
74.20, 80.96
69.64, 77.41
65.49, 74.35
72.48,
81.73
ToLu 3 8B
78.41, 80.80
86.07. 89.45
47.23, 55.26
77.98. 82.43
78.99. 84.42
77.26. 83.85
71.44, 79.00
12.61. 81.43
MINISTRLL 8B INsr.
77.37, 80.03
87.32, 90.66
59.72.
67.42
13.58,
79.81
74.80, 81.29
73.23, 81.72
80.37 70.33,
78.56, 86.54
Hamseas 3 8B
73.35. 76.4
82.59, 87.09
39.81, 48.96
69.82, 75.86
72.76, 79.03
69.66. 78.4
69.63, 78.39
77.58, 84.87
Open-weight LMs (32B)
QWEN 32B"
71,22, 76.97
90.78
43.199,53 53.38
74.17.
58.51, 69.07
59.84, 69.22
74.29,
QwQ 32B1
85.63, 87.75
DIsmUL 32B
$1.06, 83.55
54.78, 62.83
Open-weight LMs (70B)
LLAMA 3.3 70B Isst.
88, 89.89
92.47, 94.46
41.511
49.47
89.94,
92.34
88.59,
91.83
82.41,
88.75
75.01, 83.38
62.39, 75.25
TULu 3 70B
83.74, 85.96
87.68, 90.54
48.48, 56.05
81.56,
85.68
80.08,
84.93
68.98,
77.43
42,33, 49.61
82,35, 88.45
Hinesns 3 70B
79.96, 82.91
87.65, 91.34
53.29, 59.89
78.77, 84.10
76.69,
82.55
76.20, 83.18
74.06. 81.91
83.39, 90.16
Proprictary LMs
CLAUDE GPT-4n
86.08, 91.23
5.77, 9.99
79.37,
19.
76.
91.90, 93.35
92.08, 94.47
74.21,
95.27,
92.98,
87.16,
Ours: PIC-LM
PIC-IMMSTTOM
85.75, 87.71
85.34, 88.88
69.17, 74.42
84.38, 88.78
79.16, 85.20
73.14, 14, 81.38
75.98, 85.83
992.27, 95.56
PIC-LM
93.56, 94.65
95.12, 96.79
78.35, 83.07
94.05, 95.99
92.57, 95.45
81.94, 89.38
41.75, 49.23 95.50, 97.73
mode and use temperature of 0.6, top-p of 0.85, top-k of 20, min-p of 0, presence penalty of 1.5, and a repetition penalty of 1.2. We strip out intermediate thinking components prior to evaluation.
Prompt templates. For strict and fair cross-model comparison, we use same prompt template across all model baselines and tasks. Table 10 shows the prompts used throughout for PIC training
and evaluation.
We explored alternative prompts and found PIC-Bench to be largely prompt-insensitive. For example, we re-ran evaluation baselines using an alternative instruction pair:
You are an assistant that strictly adheres to given instructions. Be sure to incorporate each provided input claim in your response, and do not introduce or omit any claims.
Full PIC:
Partial
PIC: You are an assistant that strictly adheres to given instructions. Respond using any subset of given claims that you find relevant, but do not introduce any new claims.
We report the absolute performance change (%) for PIC-LM 8B and three baselines (Llama 3.1 8B Inst., Tulu 8B, and Ministral 8B Inst.) Table TT As shown, changes in PIC performance are relatively small across prompt variations, with most metric shifts <4%. These results indicate that PIC-Bench evaluations remain stable across reasonable prompt variations. While certain prompts may slightly improve absolute scores, we believe they are unlikely to affect the relative ranking between models, which is our primary evaluation focus.
B.4 Human evaluations
One reasonable concern is whether the automatic PIC evaluation corresponds well with human judgment. Specifically, an LLM is invoked at two stages of our evaluation pipeline: claim extraction (decomposing text into verifiable claims) and claim verification (checking that a claim supported by a set of verifiable claims). For both stages, we conduct a pilot human evaluation experiment to assess agreement with human annotation across PIC tasks and three baseline models (Llamm 3.1 8B Inst., PIC-LM 8B, and GPT-4o). We use three human annotators per component.
32

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Table 10: PIC prompts used for full and partial settings.
PIC Setting
Prompt
FULL

 Complete the instruction and include **exactly all** of the following claims-no more, no fewer. Your response must be entirely grounded on the given context while also answering the question.
Instruction:
(instruction)
Claims:
{claims}
Response:


PARTIAL

 Complete the instruction using ** subset** of the following claims, if they help your answer. Do not introduce any new claims beyond what's 's given. Make sure that your response is grounded on the given context, while also answering the question.
Instruction:
(instruction)
Claims:
{claims}
Response:


Table 11: Absolute performance changes (%) for PIC evaluation different model baselines, when evaluated using the original prompt (Table I0 versus an alternative prompt pair.
PIC Metric (%)
Llama 3 8B INST.
TULu 3 8B MINISTRAL 8B INST.
PIC-LM 8B
Avg. F1
1.8
1.1
-2.4
0.4
Avg. Perf. F1
0.4
0.0
-0.4
3.2
Avg. Prec.
0.8
3.2
1.2
0.4
Avg. Perf. Prec.
-0.3
0.1
3.0
4.2
Claim extraction. For claim extraction evaluation, we sample generations per PIC task and baseline model, leading to 120 samples in total. For each sample, we show the annotator the generated output and an extracted claim from that output.
Recall that for claim extraction, the LLM is prompted to extract high-quality, verifiable claims from source task. Here ask the annotator (0-2, 0 being the best) along the following dimensions: faithfulness (0: definitely supported, 1: partially supported, not supported). deconfextualization (0: self-contained, ambiguous, context-dependent), and quality (0: fluent, 1: minor readability issues, 2: hard to read).
the extracted claim a 3-point ordinal scale
observe fairly strong LLM- agreement (and substantial exact inter-annotator agreement) across all three axes, which confirms that our LLM judge (GPT-4o mini) is able to correctly extract high-quality, verifiable claims.
From Table 12,
Claim verification. For claim verification evaluation, we sample 3 generations per PIC task and baseline, leading to 72 generations in total. For each sample, we provide the list of input claims, and response claim that the LLM--ased claim verifier has marked as either supported unsupported. We ask the annotators, who are blind to model identity and the verifier's label, to judge the response claim is supported unsupported by the input claims.
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Table 12: Human evaluation for PIC claim extraction. We report (i) -human agreement (the average fraction of annotator ratings equal to the best score (0) for the LLM-extracted claims), and (ii) exact inter-annotator agreement (IAA; the fraction of items where raters chose the same label).
Dimension / Metric
L.M--uman Agreement
Exact IAA
Faithfulness
97.5
94.1
Decontextualization
89.6
76.5
Quality
99.7
99.2
We found high average human agreement with the LLM (81.0%), and substantial inter-annotator agreement (a Fleiss' Kappa of 0.694).
B.5 Few-shot baseline results
We show few-shot results for select baselines PIC-Bench in Table 13 (full setting) and Table [4] (partial setting). Since we benchmark only instruction-tuned models that have specifically been trained to respond to human instructions in zero-shot fashion, we do not consider few-shot exemplars to be necessary; still, we include them for comprehensiveness.
Few-shot exemplars lead to degraded performance in 32B open- ppen-weight reasoning models across all settings, likely due the absence of explicit step-by-step reasoning, which these LMs are trained to leverage. For the non-reasoning models, few-shot exemplars generally help full PIC, but hurt partial PIC. In the full PIC setting, few-shot prompting consistently improves average PIC-scores across all baselines. This trend reverses in the partial setting: adding few-shot examples lowers average perfect-score precision, suggesting that exemplars may distract the LM. By definition, partial PIC samples include irrelevant claims that do not belong in the target response. Few-shot exemplars, especially if they reference irrelevant or noisy subsets of claims, may introduce further distraction and make it difficult for the LM to ground its generation intended context.
Table 13: Few-shot PIC-Bench results (full setting) for select baselines. Metrics include F1, the proportion of perfect Fj (Perf.), and the average both metrics. Best values bold. For all metrics, the higher the better.
ENTBpIc Fi Perf.
PorB-PPic
PorB-CFpic
ASkHIşı
ELISpiC Fi Perf.
ExpQApIc Fi Perf.
Avg. Fi Perf.
Fi Perf.
Fi
Perf.
Fi Perf.
wpen-weight L.Ms (8B) + 2-shot
LLAMA 3.1 INst.
72.4
1.1 89.8
15.3
22.2
0.0
80.2
1.3
80.0
11.6
77.1
11.8
70.3
6.9
TULU 3 8B
78.1
91.7
10.8
61.4
0.9
82.5
1.3
84.0
13.0
78.3
11.8
79.3
MINISTRAL 8B INST.
76.5
1.1
83.4
16.2
73.4
0.9
82.3
4.4
79.5
12.3
80.6
15.1
80.9
8.4
ween-weight reasoning LMs (32B) + 2-shot
QWEN 32B
46.4
2.2
3.33
1.41
8.24
0
23.4
0.7
16.3
0.7
16.5
0.6
QwQ 32B
76.6
4.4
58.4
28.1
0.9
47.6
3.8
47.3
6.9
39.5
7.2
49.6
5.2
RI DISTILL.
85.7
3.8
57.3
13.5
42.2
0.9
39.9
1.3
54.4
6.9
45.1
11.8
54.1
6.4
Proprietary LMs + 2-shot
CLAUDE 3.5
92.4
10.9
94.4
27.9
25.4
0.9
94.4
26.0
92.8
43.2
90.2
44.7
81.6
25.6
GPT-40
91.1
6.0
94.7
30.6
79.5
3.6
95.4
41.1
93.7
49.3
91.5
52.0
91.0
30.4
B.6 Thinking mode ablations
Reasoning language models are trained to perform explicit chain-of-thougee reasoning steps [19] often through the use of special control tokens that signal the model to engage in intermediate "thinking" steps before producing a final answer [109]. We ablate the effect of the thinking mode on PIC-Bench with the 32B reasoning LMs (Qwen 3 32B, QwQ 32B, RI Distill 32B). In the non non-thinking
34

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Table 14: 2-shot PIC-Bench results (partial setting) for select baselines. Metrics include precision (Prec.), the proportion of perfect precision (Perf.), and the average both metrics (right-most column). Best values in bold. For all metrics, the higher the better.
FACTSpIC
XSUMPIC
Avg
Avg
Prec. Perf.
Prec.
Perf.
Prec.
Perf.
Open-weight LMs (8B) + 2-shot
LLAMA 3.1 8B INST.
66.6
14.0
70.4
48.0
68.5
31.0
TULU 3 8B
68.8
5.3
59.4
39.5
64.1
22.4
MINISTRAL 8B INST.
72.6
21.3
75.7
60.0
74.1
40.7
O Open-weight reasoning LMs (32B)
2-shot
QWEN 3 32B
63.3
25.3
5.9
1.5
34.6
13.4
QwQ 32B
73.7
12
75.5
50
74.6
31.0
RI DISTILL 32B
78.1
22.6
41.6
27
59.9
24.8
Proprietary LMs 2-shot
CLAUDE 3.5
86.4
49.3
89.6
73.0
88.0
61.2
GPT-4o
87.3
46.7
91.0
81.0
89.1
63.8
mode, we decode using temperature 0.7, top-p of 0.8, top-k of 20, min-p of 0, a presence penalty of 1.5, and repetition penalty of 1.2 [09].
From Table 15 observe that the thinking mode substantially improves PIC performance across nearly all tasks. Notably, every full PIC task benefits from the thinking mode, and XSUMPIC the only task where the effect mixed. While further exploration is warranted, we hypothesize that the thinking mode likely helps on PIC, constrained factual recall task, as it enforces the LM's decoding behavior be more deliberate, structured, and less prone shallow pattern completion, minimizing fluency bias.
Table 15: Comparison of 32B reasoning LMs on PIC-Bench with and without thinking mode enabled (T = Thinking, NT Non- Non-thinking).
ENTBric
PorB-Pric
PorB-CFric
AsKHyic
ELI5pic
ExrQApic
FACTSpic
XSUMpic
F. Perf.
P
Perf.
Fi
Perf.
Fi Perf.
Fi Perf.
Fi Perf.
Prec. Perf.
Prec. Perf.
Open-weight LMs (32B)
Qwen 32B
74.1
2.7
0.9
63.6
17.3
78.5
QWEN 3 32B (NT)
34.6
0.0
60.4
0.9
27.2
0.0
41.8
1.9
43.2
4.8
31.7
4.0
38.4
28.0
84.3
64.5
QwQ (T)
86.7
1.6
87.6
2.7
64.1
1.8
79.6
80.0
80.9
12.5
70.3
12.7
86.9
QwQ 32B (NT)
62.5
0.6
74.8
3.6
26.5
0.0
73.2
3.2
71.0
2.7
69.5
4.0
68.5
5.3
81.5
60.0
R1-QWEX-32B (T)
82.3
1.1
87.5
9.0
58.8
0.9
81.1
4.4
82.2
10.3
80.0
13.2
78.6
6.5
71.1
26.7
RI-QWEN-32B (NT)
71.9
1.6
86.0
7.2
41.0
0.0
77.1
1.9
73.7
0.7
76.4
6.6
73.6
8.0
87.3
17.5
B.7
Qualitative task examples
Example PIC-Bench instructions for each task are shown in Table 16 Table
17
Table
18
Table
19
Table 20
Table
Table 22
B.8 Llama 3 scaling analysis
To study the effect of PIC single model family, run five sizes (1B, 3B, 8B. 70B, 405B) of the Llama 3 model family on PIC-Bench. Fig. Wig-3]shww scaling trends. We observe that full PIC exhibits an inverse U-shaped performance curve, with F1 peaking the 70B mark, suggesting that full PIC
35

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Table 16: ENTITY BIoSPIc example.
Instruction
Generate factual biography about Donald Featherstone.
Context Claims
Donald Featherstone was born on January 25, 1936.
Don Featherstone's s signature was removed from the pink plastic flamingo in 2001.
Don Featherstone kept 57 plastic flamingos on his back lawn.
The color pink popular in 1958.
The pink flamingo went on sale in 1958.
Don Featherstone held the position of president of Union Products until he retired in 2000.
Plastic flamingos appeared across the United States.
The plastic flamingo became
popular time.
Don Featherstone grew in Berlin, Massachusetts.
Don Featherstone resided in Fitchburg, Massachusetts.
Don Featherstone was born in Worcester, Massachusetts, in 1936.
In 2010, Cado Products purchased the plastic molds for the pink flamingos.
Donald Featherstone was an American artist.
Donald Featherstone died June 22, 2015.
New York company purchased the molds for Don Featherstone's 's flamingos.
The New York company subcontracted production of the flamingos Cado Products, company located Fitchburg.
Donald Featherstone is most widely known for his creation of the plastic pink flamingo in 1957.
Don Featherstone's s signature stayed on the pink plastic flamingo until 2001.
In November 2006, Union Products closed.
Plastic flamingos were included as parts of various exhibits.
In November 2006, production of the flamingo stopped.
Don Featherstone sculpted over 750 different items while Union Products, Inc.
Don Featherstone died from Lewy body dementia.
Cado Products continues to manufacture pink flamingos.
Don Featherstone began his tenure president of Union Products in 1996.
1996, Don Featherstone was awarded the Ig Nobel Art Prize his creation the pink flamingo.
Don Featherstone died on June 22, 2015.
2010, Cado Products purchased the copyrights for the pink flamingos.
Donald Featherstone created the plastic pink flamingo while working for Union Products.
After graduating 1957, Don Featherstone was offered a job designing three-dimensional animals for Union Products, Inc.
The first two items sculpted Don Featherstone girl with water and boy with dog.
Don Featherstone hased his creation of the pink flamingo on photographs of flamingos from National Geographic.
Don Featherstone was not to obtain real flamingos to use as models
creation.
In 1987, Donald Featherstone inscribed his signature in the original plastic mold of the plastic flamingo.
The replacement
signature was a small boycott of unsigned pink plastic flamingos.
In 1957, Don Featherstone was asked carve flamingo.
Don Featherstone graduated the Worcester Art Museum's art school 1957.
Don Featherstone was 79 years old the time of his death.
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Table 17: PoPBios-PPic and PopBios-CFry examples.
Instruction
Generate a biography on (entity), ), the author who wrote War and Peace, Anna Karenina.
Context Claims (Parametric Entity: Leo Tolstoy)
The publication of War and Peace was significant event in the career of Leo Tolstoy.
War and Peace is monumental novel.
War and Peace captured the essence of Russian society during the Napoleonic Wars.
The novel War and Peace had immediate success.
The success of War and Peace cemented the reputation of (entity) a literary genius.
Anna Karenina is a novel that explores themes of love, betrayal, and the search for meaning.
Anna Karenina soliditied Tolstoy's place the literary pantheon.
The opening line of Anna Karenina is "All happy familics are alike; cach unhappy family is unhappy in its own way."
The opening line of Anna Karenina became iconic.
The opening line of Anna Karenina encapsulates the insight of Leo Tolstoy into human nature.
Anna Karenina a novel that critiques the rigid structures of Russian society.
Anna Karenina serves social commentary the consequences of defying socictal norms.
Context Claims (Counterfactual Entity: James Clerk Maxwell)
The publication of War and Peace significant event in the career of James Clerk Maxwell.
War and Peace a monumental novel.
War and Peace captured the essence of Russian society during the Napoleonic Wars.
The novel War and Peace had immediate success.
The success of War and Peace cemented the reputation of (entity) literary genius.
Anna Karenina a novel that explores themes of love, betrayal, and the search for meaning.
Anna Karenina soliditied James Clerk Maxwell's place in the literary pantheon.
The opening line of Anna Karenina is "All happy families are alike; each unhappy family is unhappy in its own way."
The opening line of Anna Karenina became iconic.
The opening line of Anna Karenina encapsulates the insight of James Clerk Maxwell into human nature.
Anna Karenina a novel critiques the rigid structures of Russian society.
Anna Karenina serves as social commentary on the consoquences of defying socictal norms.
capability does monotonically scale with model size. Conversely, partial PIC performance strictly improves with model size, with the largest increase between the 3B to 8B range.
B.9 More PIC-Bench plots
shows precision vs. recall heatmaps for full PIC basclines. We observe that GPT-4o and Claude 3.5 achieve consistently high precision and recall across all tasks, while open-weight LMs lean conservative, with lower recall (this is especially apparent on ENTITYBISPPI,, a task with 50 claims average). CorBros-CFpp is the most challenging task, with deflated scores across all settings.
Fig.
Fig. 5) shows the distribution of supported and unsupported claims for all PIC tasks. note some trends: Llama 3.1 8B Instruct is the most verbose, generating the most claims by a clear margin all tasks except ENTITYBrOspn and FACTSPIC. Proprietary LMS display a lower proportion of unsupported claims.
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Table 18: AsKHISSTORIASSPIC.. example.
Instruction
Answer the following question: I've seen Japanese artwork from the Edo and hefore depicting Tigers. Did tigers ever inhabit the islands Japan? not, how might a Japanese person encounter tiger before the Meiji era?
Context Claims
Tigers never inhabited the islands of Japan.
Japanese people became familiar with the image of tigers through cultural exchanges with China.
Japanese people became familiar with the symbolism of tigers through cultural exchanges with China.
In Chinese culture, tigers are featured as one of the twelve signs of the zodiac.
Chinese culture, tigers are one the four mythical creatures known as shisho.
In Chinese symbolism, the tiger represents the west.
Chinese symbolism, tiger represents autumn.
In Chinese symbolism, the tiger represents the metal element.
Chinese symbolism, dragon represents the east.
In Chinese symbolism, the dragon represents the heavens.
The duality tiger and dragon celebrated in ancient texts like the Ching.
In Chinese culture, dragons are associated with prosperity.
Chinese culture, dragons are associated with divine rulers.
In Chinese culture, tigers symbolize earthly power.
Chinese culture, tigers symbolize agility.
Japan adopted many cultural elements from China from as early the 1st century CE.
symbolic significance of tigers was one of the cultural elements adopted by Japan from China.
Cultural importation in Japan involved exchanges of Buddhist beliefs.
Cultural importation in Japan involved exchanges Daoist beliefs.
Cultural importation in Japan involved exchanges of Confucian beliefs.
Cultural importation in Japan involved practices like yin-yang (onmyödō).
In Japan, tigers came to symbolize military might and authority.
Japan, tigers were adopted by warrior elites to represent strength.
The Mahasativa Jataka is Buddhist narrative.
Chinese-style paintings were brought to Japan by Zen emissarics.
Zen emissaries brought Chinese-style ink paintings to Japan.
Mahasattva Jataka enriched the tiger's symbolic presence Japanese art culture.
Chinese-style ink paintings enriched the tiger's symbolic presence in Japanese art and culture.
Toyotomi Hideyoshi led Korean invasions the late 16th century.
Japanese artists predominantly relied on existing Chinese representations when depicting tigers during the Edo
Tigers were never native to Japan.
Tigers played significant role in Japanese and culture.
Chinese traditions strongly influenced Japanese art and culture.
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Table 19; ELI5piC example.
Instruction
How come horses were so common all over the world in the past? I recently watched documentary about a brief history of each continent when I noticed horses were really common everywhere for example Genghis Khan Asia, Saladin in Africa, natives in North and South America all over Europe too. know they been brought with settlers Australia because it was mentioned in documentary but what about other continents?
Context Claims
Horses evolved in North America.
Horses migrated via the Bering Land Bridge to Eurasia approximately two million years ago.
Horses disappeared from the Americas around 10,000 years ago.
Horses were reintroduced to the Americas by European settlers in more recent history.
Horses domesticated in Eurasia around 4,000 to 3,500 BCE,
Horses were domesticated on the Central Asian steppes.
Breeding and selection led to larger horses.
Breeding and selection led to more robust horses.
Larger and robust horses contributed their indispensability for travel.
I Larger and more robust horses contributed to their indispensability for trade.
Larger and robust horses contributed their indispensability for communication.
Larger and more robust horses contributed to their indispensability for agriculture.
I Larger and robust horses contrihuted their indispensability for warfare.
Genghis Khan in Asia relied hcavily on horses for his military campaigns.
Saladin in Africa relied heavily horses for his military campaigns.
Elephants require large amounts of food.
C PIC-LM Training Details
C.1
SFT data curation
To construct training dataset, consider instruction fine-tuning datasets assembled from the following general sources: No Robots [93] FLAN [T88]. as well as task-specific sources: entity biog- raphy generation (77), FACTS Grounding 42). and CNN-DailyMail news text summarization [101] (starting from a random subsample of 2000 examples). If the task does not provide gold response, use GPT-4o generate synthetic gold response since it is already pretty good at average PIC. For the training sets that are in-domain to our PIC benchmark (FACTS and biography generation), we ensure that no train-test overlap exists.
apply the following data processing pipeline:
1. Determine the PIC type based on the instruction. We do consider samples that are creative (i.e., with a low density of verifiable claims) in nature or reasoning-intensivee math or coding problems), and manually determine by label wherever possible.
intensive (i.c.,
2. Filter out samples with responses under 128 characters (as our focal point is the long-form generation setting).
3. Extract the claim list C' from the response for full PIC samples, and from the provided context for partial PIC samples.
4. Given that the claim extraction step might imperfect, and we find that this happens especially if the response is vaguely worded or in another language, compute precision, recall and F, against the list of extracted claims and the gold response. We only retain samples with non-empty claim set and sufficiently high precision or F; threshold
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Table 20: EXPERTQAPIC example.
Instruction
Why did focused naturalism and realistic notions (such proportions and perspective) appear mainly in Europe?
Context Claims
The rediscovery of classical Greek and Roman art and literature occurred during the Renaissance.
The humanist movement emphasized human experience and individuality.
The humanist movement led to renewed interest in realistic representations of the human figure.
The humanist movement led to renewed interest in spatial depth in paintings.
The humanist movement led to renewed interest in accurate portrayals of nature.
Wealthy individuals provided patronage in the arts.
Religious institutions provided patronage in the arts
Political entities provided patronage in the arts.
Patronage in the arts encouraged exploration of new artistic techniques.
Patronage in the arts encouraged exploration of new artistic subjects.
Artists began experiment with materials such oil paint.
Artists began to experiment with new materials such as canvas.
Oil paint allowed artists to achieve greater detail in their works.
Canvas allowed artists to achieve greater realism their works.
The creation of linear perspective is attributed the architect Filippo Brunelleschi.
Linear perspective transformed the way artists depicted space in their paintings.
Linear perspective transformed the way artists depicted depth in their paintings.
The growth of scientific inquiry contributed to the appearance of naturalism in European art.
The growth of empirical observation contributed the appcarance of realistic notions in European art.
Artists' workshops and academies emphasized systematic study of anatomy.
Artists' workshops and academies emphasized systematic study of light.
Artists' workshops and academics emphasized systematic study of perspective.
The systematic study of anatomy, light, and perspective contributed the advancement of naturalistic techniques art.
Naturalism and realistic notions in European art emerged due to the rediscovery of classical art and literature during the Renaissance.
The rise of humanism contributed to the emergence of naturalism and realistic notions in European art.
The patronage system contributed the emergence of naturalism and realistic notions in European art.
Advancements in artistic techniques contributed to the emergence of naturalism and realistic notions in European art.
The growth of scientific inquiry contributed to emergence of naturalism and realistic notions in European art.
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Table 21: FACTSPIC example.
Instruction
Isn't the theater required provide with an initial set-up of tea bags my housing am actor working under the terms of this contract?
Context Claims
Each Actor's housing shall be supplied with bed and mattress in good condition.
Each Actor's housing shall be supplied with nightstand.
Each Actor's housing shall be supplied with reading lamp.
Each Actor's housing shall be supplied with an armchair or sofa.
Each Actor's housing shall be supplied with table and chairs.
Each Actor's housing shall be supplied with a lamp.
Each Actor's housing shall be supplied with dresser.
Each Actor's housing shall be supplied with mirror.
Each Actor's housing shall be supplied with hangers.
Each Actor's housing shall be supplied with linens and towels.
Each Actor's housing shall be supplicd with pillows.
Each Actor's housing shall be supplied with blankets.
Each Actor's housing shall be supplied with wastehasket.
Each Actor's housing shall be supplied with radio alarm clock.
Each Actor's housing shall be supplied with a television and cable, where available and necessary for adequate reception.
The Theatre shall make available irons.
The Theatre shall make available ironing boards.
Each Actor's housing shall be supplied with pots and pans with lids.
Each Actor's housing shall supplied with cooking utensils.
Fach Actor's housing shall be supplied with silverware for four.
Each Actor's housing shall supplicd with not fewer than four plates.
Each Actor's housing shall be supplied with cups and glasses.
Fach Actor's housing shall supplied with a can opener.
Each Actor's housing shall be supplied with kitchen knives.
Each Actor's housing shall supplicd with colander.
The Theatre shall provide an initial set-up of toilet paper in the Actor's housing prior to the Actor's arrival.
The Theatre shall provide an initial set-up of paper towels in the Actor's housing prior to the Actor's arrival.
The Theatre shall provide an initial set-up of hand soap in the Actor's housing prior to the Actor's arrival.
The Theatre shall provide initial set-up of dish soap in the Actor's housing prior the Actor's arrival.
The Theatre shall provide an initial set-up of salt and pepper in the Actor's housing prior to the Actor's arrival.
Theatre shall provide an initial set-up of sugar in Actor's housing prior the Actor's arrival.
The Theatre shall provide initial set-up of coffee in the Actor's housing prior to the Actor's arrival.
Theatre shall provide an initial set-up of in Actor's housing prior Actor's arrival. arrival.
The Theatre shall provide initial set-up of garbage bags in the Actor's housing prior to the Actor's
The Theatre shall provide an initial set-up of sponge the Actor's housing prior to the Actor's arrival.
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Table 22: XSUMPIC example.
Instruction
Summarize the following text, given list of claims, in around 20-25 words.
Context Claims
Many developing countries will try to curb carbon emissions by setting aside forested areas as reserves.
Designating forest reserves in Liberia could displace many 1.3 million people.
Designating forest reserves in the Democratic Republic of Congo could displace as many as 1,3 million
people.
Liberia has proposed that 30% of its forests become protected arcas by 2020.
The proposal for protecting forests in Liberia is funded by Norway.
Democratic Republic Congo aims to set aside 12-15% of their forested lands.
The Democratic Republic of Congo is funded by Germany and the Global Environmental Facility.
Displacement has already happened in sub-Saharan Africa.
Displacement has already happened in South East Asia.
Displacement has already happened in Latin America.
Displacement sometimes caused violent conflict.
Constance Teague is from Liberia's Sustainable Development Institute.
Constance Teague stated, "I don't think the international community wants to displace rural dwellers in Liberia but think if go about in the way we talking about right now, that going be the result."
Indigenous communities respect the forest.
Indigenous communities have worked on forest hundreds years.
Liberia has the largest forest space left in West Africa.
The large forest space Liberia largely because of the indigenous communitics.
The report looks into the costs of compensating people for the loss of their lands in Liberia.
The report looks into the costs compensating people for loss of their lands in the Democratic
Republic of the Congo (DR Congo).
The costs of compensating people for the loss of their lands Liberia and DR Congo range from $200 million more than El billion.
Mr. White said, "We need to make evidence available that makes it clear that the woods are full of people, and makes
help them rather than kick them out." "
Where indigenous peoples' rights are protected, they are able to use their forests for their own livelihoods.
Indigenous peoples have carbon per hectare than protected
Approximately 1.5 billion indigenous people inhabit or claim most the land the world.
According study released last year, indigenous people have legal rights to just 10% of the land they inhabit claim.
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Figure 3: PIC scaling trends.
PIC Trends across Model Size
Full PIC Tasks
100
Avg.
600 Ang. Port
80
60
40
Partial PIC Tasks
-00- Avg. Prec.
- Avg. Berf Prec.
Precision
60
40
20
18
3B
8B
708
405B
Model (Dillion Parameters)
(depending on PIC type). The threshold 1.0 for every sample except biography generation samples, where relax the Fi threshold to 0.9.
After data processing, our dataset consists 2906 full PIC samples and 1501 partial PIC samples. Fig. (6) visualizes the task distribution of our data mix. Long- form generation, summarization and closed- book QA form the majority, encompassing 69.6% of our training data. Table 23 shows examples from SFT dataset.
C.2 Preference data creation
Algorithm sketches out the protocol used for preference data creation. Note that for simplicity, the algorithm assumes set and Pmax for the entire dataset; in practice, we assigned different values of 7 and Pmax based on the PIC setting.
Sampling Sperturb- We sample Fppentubb from OSFT is cheap proxy for more expensive, API-based LLMs. We find that the perturbed responses from OSET already exhibits strong PIC behavior as it has seen similar samples during SFT.
To empirically validate the quality of using fser, we randomly sample 50 full and partial PIC from preference data where the perturbed sample is preferred, and find high PIC scores (84.3% F, for full PIC, 86.5% precision for partial PIC): these numbers are competitive with GPT-4o sampling.
While not perfect, these signals are still valuable for preference tuning, as recent work (e.g., the Delta Learning Hypothesis (3T) suggests that the relative delta between chosen and rejected responses more important than absolute response quality.
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Figure 4: Precision and recall heatmaps for the six full PIC tasks.
Precision Recall for Full PIC Tasks
Predision
En
86.1
89.2
89.6
88.8
90.0
88.8
94.6
91.8
90.3
93.7
97.2
PopB.
PopB. (CE)
39.0
39.9
52.33
81.7
-60
Askilist.
82.2
82.7
84.5
75.8
63.7
71..7
77.0
90.8
79.2
80.2
87.2
96.1
-40
ELJS
73.5
80.1
81.6
78.0
58.3
70.0
67.3
88.2
75.7
76.7
88.0
96.0
ExperIQA
72.0
77.8
79.4
73.1
43.5
73.4
73.0
84.
73.8
78.3
70.8
91.9
100
EntityB.
67.1
66.0
62.1
84.6
79.6
75.5
85.0
PopB. (Para.)
Popli (CF)
35.8
61.2
63.9
51.8
21.2
30.5
46.0
57.5
60.1
66.7
10.1
71.8
60
AskFisst.
84.3
80.7
75.2
75.9
33.7
74.6
81.0
92.5
90.9
86.3
82.55
96.7
-00
2L1S
87.8
86.5
79.3
79.4
38.7
71. 8
85.77
4
93.77
88.7
85.6
94.1
ExpertQA
83.3
88.1
80.5
87.6
92.0
79.9
86.3
62.8
Ulana 3.1 an tnst.
Minisral on Inst.
Hormes BB
Owom 328
Owg 328
RI Disutl 328
1lama 3.3 708 Inst.
Tuta 708
Harmes 3 70B
Clando 3.3 Sonant
GFT-40
Tala
Figure 5: Histogram per-baseline supported and unsupported claims for each PIC task.
Seppestidd Slasspaarted Claies for PIC Tadls
Suppored
Unargered
Pige:
Draki
Oiperigs
ANCES
No
44

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Table 23: Examples from PIC SFT data.
Source/ Instruction
Verifiable Claims
Response
(CNN DailyMail, Par-
Smudi Amaii is the world's is largest oil producer.
Swudi Anbin, the world's top oil producer, carned roughly $200 billion from ail last driving national growth. How- ever, inflation surged to 30- year high of 9.6%, In Manch, and water prices rose by nearly 16%, while other staplc goods also saw double digit in-
Gil PtC)
The carnings from oil Eucked national growth in Saudi Arabia.
Summarize the follow-
the previeus year, Soudi Amhin carned around $200 billion from oil.
ing text, given as a
Inflation Saudi Arahia reached 30-year high 9.6%.
claims, in around 5D
In March, there were price incresses of nearly 16% for fhel and water. Murch, other staplss experienced double-digi cosi incresses.
worth.
Roboes, PIC)
Over a quaster of cars on road (25, 9%) are white.
an interesting question. Most Americans seem to prefer monochromiric coloes when comes their vehicles. top five most common car col- ors are:
What the most com--
The top live most common car colors include white.
mon color car in Amer-
Over quarter of the cars the read are white, specifically 25.9%.
ical?
Black the second moot common car color in United States at 22.3%.
Gray is the third most commmnn car color.
18.1% of cars on the read are gray.
12.1% cars the red are silver.
quarter of chre on road (25.9%) are white.
Whiie
Silver the fourth most common car color the raad.
Blue the only non- onn-monochromatle color in the top five car colors,
9.5% of cars (he rodd are blue.
Black close second 22.3%.
among car colors.
3. Gray is third with 18.4%.
4. Silver is fourth with 12.1%. the only
monochumaiic color the top five, is fifth at 9.5
(FACTS, Partial PIC) the people thought would huppen in the future accord-
Thee article titled "Hype alert: Why cyberspace isn't, and will never be, zirsona" was published by Newswss.
Based on the provided claims, the things people thought would hoppen in the future according to the 1995 ar- ticle:
The magazine issuc containing the article is dated February 27, 1995.
Visionaries see a future of telecomuuing workers.
ing this article from 1995.
Visionaries
future of interactive libruries
Visionaries sre a furare of multimedia clascroons.
Telecommutinng would be- come commonn for workers.
Visionarios speak of electronic town meetings.
Visionaries speak of viriual comminiries
2. Interactive libraries would emerge.
Uwene worldwide bulletin bound.
The Usenet allows anyone to post messages across the nation.
Multimedia claserooms
The cacophonyy resembles citizens hand millo.
would develop.
Citizens band radio includes handles.
Electronic meetings
Chiizens radio includes harasement.
would take place.
Citizens bund radio includes anonymous threats.
5. Virtull communities would form.
Nicholas Negroponic is the director of the MIT Media Labh
Nicholas Negroponte predicts that people soon buy books and newspupers struight over
People would buy and newspapers direcily over the In- ternet (is predicted by Nicholas Negmoponte).
the Internet.
The Internet locks editoes, reviewers crities,
The Internet
wasteland untiltered data.
The Battli T'rrfalgar is a historical event.
These prodictions come from the "visionaries" and Nicholas Negroponte. described the Nawswarx article.
Internet addicts clamor for government reports.
Andy Spuno
county executive in Westchester County, New York. Andy Spano put every press release position paper onto balletia board during his
campaign.
Westchester County, N.Y.
allloent county.
Westchester County, has plenty computer companics.
Fewer than 30 voters logged in during Andy Spuno's campaign for county executive in
Westchester County, N.Y.
Computer sided education tools are expensive.
Computer-aided education tools are difficult use classrooms.
Computer-sided oducation tools requiee extensive wecher truining.
Ablating L in score computation. Following Chuang et al. [1,, compute the average per-token log-likelihood over the last L = 20 tokens of each response to reduce computational cost and length bias; under this setting, the shorter response is chosen roughly half (49.0%) of the time, as desired.
We ran an ablation where instead of computing the per-token log probability over the last tokens, we consider the entire response sequence instead. This results in length bias, with the shorter response preferred 60.2% of the time. Training on length-imbalanced preferences deteriorates average PIC performance 100% vs. 91.4% for full, and 92.6% vs. 93.7% for partial PIC) and Prometheus scores (3.89 vs. 3.92). Thus, we retain last-L-token scoring for its balance of efficiency and performance. Alternative normalization strategies, e.g., length-aware scoring, or an adaptive L. may improve robustness; we leave this to future work.
Ablations on Recall that for PIC preference data construction, we choose between original and perturbed responses based on whichever one has better instruction adherence. If the perturbed response is significantly worse at cdtructioo-followinn than the original response, then it ought
45

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PIC-LM Training Data Distribution
Explanation/Deeiiitoon
Fact Finding
O pen QA
9.6%
Brainstorm
Other
5.2%
10.7%
3.6%
15.4%
Closed QA
32.0%
Generation
22.2%
Summarize
Figure 6: PIC-LM training data distribution.
to be dis-preferred. We set as a threshold for the log pog-prbbability difference. We ablate over (0.0, 0.3, 0.4, 0.5, 0.6, 0.7, 1.0], well as RANDOM baseline (where the preference is chosen random between original and perturbed responses at each sample), for PIC scores (F1 and precision), instruction-follon following (average Prometheus score), and average EM on ASQA. Intuitively, higher r raises the tolerance for how damaging the perturbation must be (i.e., how high the normalized peeprrbbayility drop
prefer the original construction. At 5 always chosen, and the perturbed construction is always rejected. At r = 1, the vice versa happens.
the original construction is
As Table 24 shows, performance curve. The Prometheus score also exhibits slight inverse U-shaped curve as T increases, while the average EM for ASQA strictly decreases. The RANDOM baseline shows comparable PIC and average EM performance, but lower Prometheus compared to the 7 0.5 setting, underscoring the utility of more principled approach preference data construction. Given these results, construct our preference data by setting T = 0.5 in the full PIC setting, and 7 0.3 for the partial PIC setting.
increases, both full PIC and partial PIC experience inverse U-shaped
LLM-as-a-judge validation of pairwise preference data. Employing a powerful LLM-as-a-juns LLM-as-a-judge (e.g., GPT-4.1) to arbitrate between responses the gold standard pair-wise wise instruction-folloving evaluation. However, doing so incurs significant API costs. One may also use dedicated reward model to predict instruction adherence scores, but this requires labeled preference data for training and careful calibration to avoid its own biases [2
46

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Algorithm PIC Preference Data Creation (DSFT. OSFT. Opet. T, L, Punin Pmax)
Require: SFT Dataset DSFT {(I,, Ct, (s)) SFT model OSFT, reference model RRes threshold [0. 1). token window size L, and Pmins Preax s.t. O < Pania & Pmax S
Ensure: Preference dataset Preer = { (Zz, Ci, VE 96 m==
1: Initialize Pper +
2: function Posor
P (C)
3: A |C|
& is the number of input claims
4: if k then
return C
nothing (or only one item) to drop
end if
7: Mmii + -max(1, [pmin k))
8: Mmma < min(k 1, [paax &I)
9:
if Mmin > maxx then
10:
Mynin Mmax
11: end if
Sample m ~ Uniform Mimin mmmx)
m number of claims to drop
Randomly remove m claims from C' to obtain @pertub
14: return (.perturb
15: end function
16:
for each (I,, C,, 24) in Dsrt do
17:
Ogeniat + PPrpp(C)
18:
yf pertich + OsFr(I, C.emah)
19:
Compute the per-token prooprbbayylity (last L tokens);
log Pona (y)
Iz); getuuh
log Pone (vt
pemutt( Iz)
20:
Compute the normalized log- pe-pobbability drop:
e
A. C perturh
= o(ii) 1 E.
21:
if z: > then
22:
Append (Ix, Ci yes y. g to Prrt
23: else
24:
Append (I,, Cpertur periute, (.) to D
25:
end if
26:
end for
27:
return Ppot
By comparison, our probability drop metric is cheap and automatic, and only relies competent enstruction-folloiing LM (we use off-the-shelf Llama 3.1 8B Instruct). To validate that aligns with LLM judgment, we:
1. Subsample 200 pairs from our training data with a log-probability drop that exceeds thresholds
7 E {0.5, 0.7, 0.9).
2. Re-score each original vs. perturbed response using GPT-4. as the judge.
We conduct this process thrice using different random seeds (0, 21, 42), with randomized response ordering (to avoid presentresnnttionn bias) and following AlpacaEval's 's [62) prompt and generation settings with tie support. the normalized log probability drop reliably captures eesstrcttio-followwii fidelity, 1PT-4.1 should prefer the original response often than the perturbed response.
Empirically, GPT-4.1 prefers the original response 80.2% 2.8 cases at T = 0.5, 77.6% 2.6 T= 0.7, and 71.3% + 1.2 at 0.9, indicating strong agreement and validating the signal captured by our automatic metric. We hypothesize that the drop in agreement at higher thresholds may stem from perturbed responses that are heavily penalized by the LM for diverging in stylistic variance or form (which is empirically consistent to findings from Eisenstein al. [23 yet remain semantically valid or even preferable to G.T-4.1.
Normalized log-probability drop distribution. Fig. shows the distribution normalized log- probability differences (between original and perturbed responses) over preference data. Intuitively,
47

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Table 24: Ablations using average PIC F1. precision, average Prometheus (instruction- following), and average EM on ASQA (retrieval- QA). The RANDOM setting uniformly samples between responses. For all metrics, the higher the better, and best values are in bold.
Setting
(Full) PIC
(Partial) PIC Prec.
Prometheus
Avg. EM
0.0
88.5
92.8
3.78
66.6
0.1
88.5
93.0
3.78
65.6
0.3
90.1
93.1
3.82
63.2
0.4
90.6
91.2
3,89
61.0
0.5
91.0
91.9
3.92
58.3
1.0
90.6
RANDOM
90.3
92.0
58.3
higher values the x-axis indicate that the perturbed response is substantially worse than the original (as judged by a reference model).
We note that the data distribution has peaks near both ends of the range (i.e., near 0 and I); this suggests that there are some samples for which the original response favored with high confidence in terms of instruction-follooing quality, and vice versa for the perturbed response the other end of the spectrum. The nature of this distribution motivates the use more informed strategy for selection preference data, and may explain why the random baseline underperforms compared to choosing preference data using the normalized bog-probabiiity drop score.
Normalized Log-Prob. Drop Distribution
Full PIC (mean: 0.55)
250
Partial PIC (mean: 0.49)
200
Sample Count
150
100
50
0.0
0.2
0.4 Normalized Log-Prob. Drop
0.6
0.8
1.0
Figure Normalized log-prob score distribution on preference data.
Table 25 shows preference data examples.
48

---

C.3 Training settings
Implementation details. We conduct training with full parameters using the accelerate pack-
age [36] and DeepSpeed Zero-3 3 Offload [96] on cluster of 40GB NVIDIA L40 and A40 GPUs. For both SFT and length-normaile normalized DPO stages, we use the AdamW optimizer [69 and cosine learning rate schedule.
Loss equations. To produce OSFT, we fine-tune an instruction-tuned LM with parameters 0. Given each triplet (Z, v) ~ DSFT, we minimize the standard teacher-forcing loss:
0SFT arg min B(I,C.a)-Dsr
log no(u I, C, C.ycs)
Following Lambert et al. [52]. employ length-normalized DPO objective. Given each tuple (I, C,C. . ) ~ DDPO.
@roo arg max B(I,C.a- 1~Doo log B (y II,C) 3 Toam (y (I,C)
1y-1 Toam (y (I, )
log To T C)
log
18
where OSFT the fixed reference model, and 3 > O is scaling hyperparameter.
Hyperparameters. For SFT, we use a batch size of 128 and a learning rate of 1e and train for epochs. We grid-search over the learning rates {le 5, 1 2,2e-6) "), batch sizes (32, 64, 128),
and fix to weight decay of 0.1 and epochs (training for more epochs causes the model to overfit and preferentially emit the unperturbed gold response, which breaks preference data creation by eliminating variation between outputs).
For DPO, we use learning rate of - batch size of 128, weight decay of 0. B of 5, and train for epoch. We grid-search over the learning rates (5e-7,80 1..8e 8e, 1e and batch sizes {32, 64, 128).
C.4 PIC-LM ablations
We ablate some design choices behind our PIC-LM training recipe in Table 26
SFT data quality. During SFT data construction, we retain only samples with a sufficiently high PIC score (either Fi precision). We find that quality filtering meaningfully improves both PIC and instructionfoolooiing metries.
We employ dedicated SFT stage to learn a good PIC initialization, instead of only conducting DPO from an instruction-tuned LM. As Table 26 shows, skipping the SFT stage leads to degradation in both PIC and instruction-fee -follooinnnoolowinng ability, underscoring the utility of two-stage training process. Future work can explore how to combine PIC with more sophisticated mnstruction-followii data generation strategies, i.e., instruction backtranslation [59]
Incorporating SFT.
Loss ablations Finally, we explore other preference optimization objectives instead of length- normalized DPO: the original DPO [91] without the nonggth-ormmlizationn term, and SimPO [76]. an objective that does not rely frozen reference model. While all three preference objectives achieve similar PIC and Prometheus scores, PIC achieves the best PIC F and precision scores.
C.5 Instruction-follovin evaluation
Our custom instruction followwig rubric for Prometheus [49] evaluation is in Table 27 Specifically, we use Prometheus BGB 8x7b 2. as the evaluator model. Following best practices, we also supply the gold response a reference answer where provided (namely, for PopBios, AskHistorians, ELI5, and ExpertQA), which helps stabilize results. In our Prometheus evaluations, we tried to disentangle cntrruction-ollooiinn from context claim adherence much possible, although the two are inextricable in some cases (e.g., summarization is a task that by definition depends on source context).
49

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C.5.1 General performance of PIC-LM
While PIC-LM is not meant to be standalone LM, but rather a highly faithful module within larger systems for grounded generation. For transparency. we report results on the following general language tasks: MMLU [38] and ARC-C [13] using the OLMES evaluation kit [4). GSM8K (16).
and AlpacaEval 2.0 21 in Table 28
C.5.2 Qualitative analysis of PIC-LM outputs
Linguistic quality. We ran small-scale qualitative analysis of PIC-LM outputs by sampling 40 PIC-Bench instructions and comparing response from Llama 3.1 8B Instruct and PIC-LM 8B along two dimensions: coherence and fluency. Following Li et al. [60], we define coherence as the degree to which the ideas in the text flow together logically (i.e., whether the sentences are on-topic and follow a clear progression of thought). We define fluency to be how grammatically correct, smooth, and natural the text is.
For each output pair, we indicated a preference for either model, or tie along each dimension. All outputs evaluated blindly in shuffled order, without revealing model identity, reduce annotation bias.
Table 29 indicates that PIC-LM 8B outputs tend to be more coherent than Llama 3.1 8B Instruct. Qualitatively, we find that PIC-LM generations are more concise and factual, whereas Llama generations are more verbose. Both model generations are similarly fluent.
Error analysis. We manually examined 40 PIC-LM generations on PIC-Bench with imperfect scores (5 samples per evaluation task). This sample has 1165 output claims in total, of which 68 response claims are marked as unsupported (affecting precision), and 63 input claims are marked as missing (affecting recall for the full PIC setting).
We provide categorical error breakdown of the 68 unsupported response errors in Table 30
Among the 63 missing claims, observe obvious qualitative differences, but mild head-bias: 27% of drops occur within the first quarter of the list (vs. 22% in the final quarter), indicating that earlier claims are slightly more likely to be omitted, and corroborating the "lost-in-the-middle context phenomenon (65)
C.6 Domain shift generalization
peripheral benefit of PIC-LM is that its capacity for information control can effectively extrapolate unseen domains. Although PIC-LM is trained on gemeral instruction-tuning data, test if PIC abilities can generalize to BioASQ (50). biomedical QA task. This task mirrors high-stakes professional demands in the biomedical field, where information transmission errors can incur significant consequences. BioASQ serves as rigorous means to assessing the model's performance target domain [ and PIC-LM's 's ability to minimize faithful hallucination and enforce context reliability can be beneficial.
For this experiment, for summaries, lists, yes/no, and factoid questions with gold responses exceeding TO0 characters. We construct context claims by concatenating provided document snippets and applying PIC claim extraction. Since not all claims are essential for ideal responses, we evaluate in the partial PIC setting where answers should only use relevant subsets of claims.
286 samples from the 2024 BioASQ12 Task B datase after filtering
As Table 31 shows, PIC-LM exhibits non-trivial improvement across all PIC metrics on the biomedical task, and substantial margin over the open- open-weight baselines, showing that it can generalize well to unseen distributions.
C.7 Scaling PIC-LM training
To explore how PIC-LM training would scale with model size, we additionally ran training experiments initializing from IB, 3B and 70B Llama 3 Instruct models, and found strong PIC-Bench improvements
https::/ppariicppatte area.
50

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in all settings (Table 32). Note that due to computational constraints, we conducted QLoRA fine-tuning 19 for the 70B PIC-LM, and full fine-tuning for all other sizes.
D PIC Use Case Details
D.1
Implementation details
D.1.1 RAG.
We adopt the simple default of concatenating retrieved documents in-context to the input query [04]. Following Gao et al. [27]. we augment each ASQA sample with the top five 100-word passages, initially retrieved from a 2018-12-20 Wikipedia snapshot using GTR [84), dense retriever, and subsequently reranked using a greedy protocol. When computing exact match, we check for each short-form answer substring in the response generation after text normalization following Rajpurkar et al. [94]. The primary difference is that context is passed in as list of decomposed, verifiable claims, rather than as free-form text. Since exact match is strict metric that favors verbatim overlap, we append the following suffix to the instruction: Note that there are multiple possible answers; please return all of them. Answer the question by returning verbatim "*exactly all** w* of the claims that contain the desired answer.
D.1.2 Pipeline.
Pipeline overview. We describe the overall process of the factuality pipeline below, which mainly builds off Dhuliawala et al. 20 (Fig. shows end-to-end diagram, and Table 33 qualitative example):
Figure 8: Self-checking factuality pipeline.
Instruction
CelffConssitency Answers
Name politicians
Sen California.
Lofgren was bom
Zoe Lofgren born Mateo, CA.
Generate
Answer each
Zoe Lofgren was born
Draft Output
Draft verification questions
verification
question
Zoe Lofgren was bom in San Francisco, CA
San Mateo,
Lofgren was born
CA.
Zoe
Where was the politician Zoo Lofgren born?
Zoe Lofgren was bom in San Mateo, CA Edwards
2. Ro Khanna
Where was the politician Ro Khanna bom?
Input Claims ................................
Don
politician Edwards
4. Norman Mineta
Where the politiolan Norman Mineta born?
5. Tom Lantos
Where was the politicinn Tom Lantos born?
2. Norman Mineta was born San Jose, CA.
1. Generate draft response: Given an instruction, generate a response from which a list of verifiable claims could be extracted.
2. Draft verification questions: Convert each candidate claim into verification question (either using the LM or in a template-based fashion).
3. Execute verification questions:
Each verification question is executed in parallel = times with self-consistene sampling, and the most consistent response (as determined by majority vote, e.g. or higher) over the runs is selected as the final answer. the response claim semantically matches the draft claim, then it is included verified claim; otherwise. it is excluded.
4. Generate final response: Given the original instruction and the list of self-verified claims, generate long- ffrm response. The response should be grounded entirely on the input claims, and should not introduce any extra information.
Following Song et al. [05], Wei et al. 120), we use the Serper AP to retrieve web-snippet evidence when scoring factual precision. A claim is considered factually correct if it is supported at least one of the top- retrieved snippet passages from Google Search.
12
"attps://sepprr.de"
51

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A small number of samples do not result in any self claims (i.e., empty output after step 3); as this is solely a deficiency the self velf-veriiiatiion LM, we exclude these samples from consideration across all settings. Thus, the Birthplace task consists of 252 samples, and QAMParl consists of 894 samples.
Birthplace task construction. To generate the instructions used in our birthplace task, we use the fol- lowing occupations: politician, artist, actor, scientist, writer, entrepreneur; journalist, professional athlete, activist, singer and the following locations: San Francisco, California; San Diego, California; Washington, D.C.; Boulder, Colorado; Philadelphia, Pennsylvania; Miami, Florida; New York City, New York; Honolulu, Hawaii; Seattle, Washington; Boston, Massachusetts; Chicago, Illinois; Baltimore, Maryland; Los Angeles, California; Austin, Texas; Phoenix, Arizona; Denver, Colorado; Las Vegas, Nevada; Portland, Oregon; San Diego, California; Atlanta, Georgia; Nashville, Tennessee; Charleston, South Carolina; Minneapolis, Minnesota; Cleveland, Ohio; Milwaukee, Wisconsin; Kansas City, Missouri; Detroit, Michigan; St. Louis, Missouri; Columbus, Ohio; Charlotte, North Carolina to populate the template: "Name some occupations born in location."
D.1.3 Context-aware baselines
Context-Aware Decoding (CAD). Context-aware decoding [022] encourages greater context reliance by contrasting the probability distributions of output with and without context. Formally, assume language model 0, an input instruction æ, and some context The standard way to sample the output sequence can be represented (at the t-th step) via
Yt x exp logit, (Y+ x, Mct))
CAD proposes upweight reliance on the context by factoring out prior knowledge from the model's 's context-less output distribution a contrastive manner, like so:
Yt softmax (1 logitt (welc, x, Merr - (yo ver)],
wherein a is hyper-parameter that controls the degree of contextual influence. We perform good-faith replication of CAD using a temperature of 0.0 and an of 0.9, which are suggested hyper-parameters for knowledge conflict tasks. Note that CAD requires two forward passes per sample, so it is twice as computationally expensive as regular inference.
Self-Cite. Self-Cite 8B [4] is an attribution LM trained to generate sentence-level citations that refer back to context in their responses. This work proposes self-supervised way to construct preference data via best-of-N sampling using self-supervised reward signal, and conducts preference optimization using SimPO 776. We use their released 8B checkpoint
D.2 Additional experiments
D.2.1
RAG
Additional experiments on ASQA. We investigate two variations of our RAG experimental setup on the ASQA dataset and compare PIC-LM 8B against prompting baselines. First, we replace decomposed verifiable claims with organically retrieved passages as input context (PAssAGE-LEVEL CONTEXT); note that this setting omits the claim extraction step and corresponds to standard in-context RAG, simulating noisy input without explicit claim structuring. Second, we use claims from the top-5 retrieved contexts withour reranking, following Gao et al. (27] (No RERANKING).
T44le
presennss presents the results. First, the PASSAGE-LEVEI CONTEXT setting, PIC-LM retains a small EM advantage despite being trained solely on verifiable claim lists, demonstrating its ability to generalize across context formats. Interestingly, some prompting baselines (e.g., Llama 3.1 8B Instruct) benefit significantly more from passage-level than claim-level context.
Second, in the No RERANKING setting, PIC-LM continues to outperform all baselines in EM across both standard and oracle regimes, though overall numbers are uniformly lower without reranking. These results reinforce the importance of high-quality retrieval for strong end-task performance.
52

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presents qualitative example from ASQA (with context claims omitted for brevity) using Llama 3.1 8B Instruct and PIC-LM 8B. Due to the strictness of exact match, PIC-LM 8B appears more effective at verbatim phrase reproduction, which the metric favors.
Qualitative examples. Table 35
D.2.2 Pipeline generation with content and arbitrary style constraints.
Recall that in our proposed pipeline approach ($ the verification step (just before the final generation step) outputs a self-verified, self-consistent list of claims that sets the upper bound of factual precision. While may be suffice to directly return this list if only factual precision matters, users often not only care about what information is contained in the response, but also how the information presented.
Transforming these claims into a freeform response enables control over tone (e.g., sentiment) and structure (e.g., bullet points). To evaluate PIC-LM's ability to produce style-controlled outputs while preserving factual content, we conduct small-scale experiment using our pipeline.
Returning to birthplace task, define six style attributes: positive, negative, polite, opinionatedd oed humorous, and satirical, which the LM is instructed to adopt in its response. We modify the final generation prompt to: "Who are
occupati ions born in location? Please answer in full sentences in a(n) [style} way," and use a subset of 88 samples.
We evaluate along two axes: (I) factual precision, following 662 and (2) style adherence, assessed by winrate against generation without specified style constraints, using GPT-4. 1 as the judge LLM. Table presents the results in the format of (factual precision, winrate). PIC-LM shows the best factual precision across nearly all styles, with notable margins in the negative, opinionated, humorous, and satirical transformations. For the polite style, Ministral 8B Instruct performs slightly better (at 90.2 vs. 88.1).
All models generally succeed in adopting the target style, with win rates over 90% in most cases (i.e., the transformed response is preferred most of the time). The negative style transformation is the most challenging; we hypothesize that the neutral tone of the concatenated claims may be misinterpreted as carrying negative sentiment. Table (7)le/3 shows qualitative examples. Notably, PIC-LM tends toward more concise and context-faithful responses (i.e., some baseline responses disagree with the verified claims) with less stylistic intensity. While some works may explicitly consider style valence when evaluating arbitrary style transfer, we do not consider this facet to be particularly relevant within the scope of this experiment [107], and defer a more careful exploration to future work.
In sum, these preliminary results indicate that PIC-LM is an effective vehicle for reliably rewriting information under style constraints.
53

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Table 25: Examples from PIC preference data.
Source /lnstruction
Veriliable Clainss
Chosen Response
Rejected Response
No Robsts Full PIC
Toui Morrison was born February 18, 1931.
Toni Morrison was bora on February 18. 1951. She died on Nugust 5, 2019. Her career American novilist chumsterized by success and awards, incluling the fillww ing:
Toui Morrison was bora Fehruzry 18, 1981, and die on Angus 5, 2019. Her eveet
Toui Macrisot ded oa Nugust 5, 2019.
When was Toai Mor
"Song Solomoon 's novel wiltten Toni Mortison that was published in
rism bum? When did she die? Whit
1977.
American sos- clst was makee by sumer- ous words and accolades. in- cluding National Book Cri-- ics Cirde Awsed for "Sung of Sohmmu" (1977) and Pulliaer Prime for "Belowed" (1987)
"Beloved" is novel writen by Tosi Morticnn that was published is 1957.
was her cureer?
"Belvved" earaed Tosi Murrisan Pulizerr Prise in 1958.
"Song Solumus" cameol Toui Moerisce a National Rock Critis Clrck: Award.
"Seng of Solemon:: This
1937 ausel curnel Muriico
National Book Oniice Cirole Award
"Beloved": This 1957 novel carned Morrison Pillizer Pnzze 1988.
Bingraphies
David Begne died on 25 Ocnober 1828.
David Bogaar a Britihh nonzon- frrmid viligions lowder, uss bora on 18 February 1750 at Hallydown Farm the parish of Colddnnhhmt, Berwodshhire. Seodlad. He was the soa of John Bogue, fammer and Margaset Swanston. David re ccived his early olucation Eyenouth and laier studied Di- vindty Ediaburgh University. In 1971, he sent by the Church of Sanihaa Lamken and was liscrecl to presch by Church of Scotian, He trught schools or Fdlmon- fon, Hampstzad, and Munsion House Corage Camberwell 1770, sented the incependent Congre- national church
Dacid Regue, hom en 18 hebrr- aey 1780,, wos a Riniich nencue- formist religious leader. He was the son of John Bogue forner, and Margieet Swansoon Dasid asseived his eaily cduza- tioa in Eyemouth.
Full PIC
Tawid Regse was hom at Hallysoon Farm the parish of Coklinghom, Berwick- shire, Scotland.
Generate factual bi ogriphy sbout David Bogne
Dondd Boggee was a Briiihh toncenorriist religons leader.
Jehn loggue was Davdd liegue's fatber
Margueet Swassion was Devid Bogue's is muther.
Dovid Bogne was bom on Exbruary 1750.
Dovid Bogne's mother was Margaret Swansson.
In 1771, David Bogue was seat by the Clunch of Sanlaadd to London, where he cstibiisted insiiution for prepainng mee for the miniery. His ausdemy becane the seall from which Lambo Missumary Sociaty By 1800), the London Miveiotary Souiety placed mis- siouuris with him mainnnng taught Williom mis- China, 1812. 1824, Sanmel Gospect before left for Penang sisuary.
Davvid Boque was the of Juhn Bugue, faruzz.
Duvid Bogue roccived his carly oducation Eyensstlh.
David Bogue uagght schools Edmonoon, Hampsteud, and Marsion House Cottgge Camberwell
Iln 1771, David Bagne acti by the Chundref Sanlard is Lambo.
-Dovid Beque was liscree preach the Chunch of Soothad.
Dorid Hogge studied Drvinity at Hidihhuggh University
In 1777, David Rogue satteed as minister the independent Cingreptional church at Gompori Hameshire
'The predecessors David Bogue the Independent Chapel Gosport James Watson, who from 1770 1726, Thomss Williams, who served from 1250 1770.
David
Milne, the first
1809
estallisted lnstuttio preparinz the from the London Misioouryy
Devid Begue's
Humpshire. succerding Watson Thomas Williams Inkeeenkut Cluycl
Society
de London Missiouary Society placed missionaries with Darid Bogue
pecpuration dicir ministries.
Britsh also helped found
David tuught William Milse, the fiss Protestam missionary
Dasid esublished minisity
Bible Religions
China
Dovil Begue 1812.
pecyueing fur
Mocrisen,
Pronsdaaii misiumer
Sucidty He
Clins
sced from wlich grow Miccionary Missinssy Society sionatess him for prepura tsught minurries
say Divinz Authority Testament" Rogas Reighton during ety' 's annual in 1825.
Blegoe would gone
for opposition
Campuny:
Sermdl Dyer Prosang saua misiooaary with Lendun Misiitary
Missioatry
Society.
Heggee Hoggee caight Samuel Dyer at Gosport.
founding Dovid Bogue was involeed Sounding Religious Tract Society.
Foreign Society
Protestant missionrry from 1505, William Milne, Photes last misionary China, 1809 1512.
James Bennett was minister Romsey.
Dasnd Bogne. conjunction
Jumes Bennett, wrote koown "Histrry
Disactters in
Duvid Bogue wrote in 'aay on
Authority New Testament.
he tangtr Sanuel Dyer Gospoct before Dyer
1815, University awarded Dovid Bogue a doctor divisity (DD).
Dhwid Begue died dering the Landon Misinney Sucidy's 's ammul lour.
Pensnng as misiimey y the London Missionary Society.
David Begue died Brighoen.
David Regue was incoleed in fannding Beitish and Fin- eign Bibte Society and the Keli pous traet Society. In conjane- fion with James liennett a min- iszr at Ronsey, he wpoee a well. known 'Histey of Dissenten" in 1808, He also authored in "Eaway on the Divine Autho- liy the New Tevameuu. In 1815, Yale University avar him a dusico divinity (Ti)) Dacid Bugue would hane gone to Indii in 1796 if for op- poetoon of the East India Com- puny. He dira on 25 Datober 1835 in Rrighton daring the London Missonary Soctety's annual tour.
Rorhots
Prinasee
The Honeerable Franccx Spensoo
eaceriginally Hittmbbl Prinsss Spencss on July 29, 1981. she Walecat St. Paslss Cathearss London.
Diano prinotes mserical Cloekos prines Wales.
Partial
1961.
Tell why
Prinssos of Wales Narfoll.
bocame proccess
Lady Dera Spencer's
Dana Sprncor
syla Laty Spencer 1975.
Spencr
Welco P in Landon
on July 1981. Diara, Princess
soss: William Prince Henry (itarry).
After her diverce from Prince
Princess Wales, continsed
regarded membeer the Royal Funily.
Diana, Princess of Wales,
Sunday 31 August 1997. Dians. Prinssos of Walcs dhid fullowing car anadi Paris.
There vas widegread public mouninne the death Diaus, Princess Wales.
The funeral Dian. Princess Wales, took place Westminster Abbey on Sanmly, September 1997.
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Table 26: PIC-LM training ablations in red. At the SFT stage, we ablate with training on unfiltered data; the preference optimization stage, ablate with (1) skipping the SFT stage and only running DPO (i.e., initializing from Llama 3.1 8B Inst.), (2) using un-normalized DPO, and (3) using SimPO.
Setting
PIC Fj
PIC Prec
Prom.
SFT
PIC- PIC-I.MsSTON
81,9
87.6
3.87
FILTERING
83.3
Preference Optimization
PIC-LM (NORM. DPO)
91.0
93.3
3.92
w/o PIC SFT
90.4
89.0
3.83
PIC-LM (regular DPO)
89.0
90.9
3,93
PIC-LM (SimPO)
90.8
93.3
3.91
Table 27: Prometheus
49
rubric for instructionffolloiing evaluation.
Criteria: Assess how effectively the long-form response adheres the provided instruction, ensuring it meets all constraints (e.g.. word limits, formatting requirements, substantive engagement with the claims), maintains clarity, and fully addresses the task.
Score
Description
Complete Failure:
Merely lists or concatenates included claims (e.g. raw concatenation, bullet points) without applying, explaining, or integrating them address the instruction.
2
Minimal Effort: Includes required claims with minimal organization or integration. Response may be off-topic, incomprchensible, irrelevant. Disorganized Incomplete: Attempts follow instructions but does inconsistently or haphazardly. May include unclear phrasing, broken structure, signs of misunderstanding constraints.
3
4
Integrates required claims into coherent structure (e.g. grouping, ordering, brief explanations) and meets all explicit constraints, though it may have minor issues in clarity, completeness, precision.
Moderate Compliance:
5
Effective Execution: Expertly weaves provided claims into a unified, well-reasonee response that fully satisfies the instruction; adheres flawlessly all constraints and exhibits clarity, precision, and depth.
Table 28: General language task results between Llama 3.1 8B Inst. and PIC-LM 8B.
Task
Llama 3.1 8B Inst.
PIC-LM 8B
5-shot MMLU Acc. (Raw Completion)
43.6
44.2
ARC-Challenge Acc. (Raw Completion)
55.6
58.7
5-shot GSM8K Exact Match
75.7
69.3
AlpacaEval 2.0 Length-Controlled
Controlled Win Rate
20.9
17.1
Table 29:
Qualitative analysis of model outputs on PIC-Bench.
Prefer Llama 3.1 8B Inst.
Tic
Prefer PIC-LM 8B
Coherence
25%
27.5%
47.5%
Fluency
2.5%
72.5%
25%
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Table 30: Breakdown of PIC output error types.
Error Type
Explanation
Count (Percentage)
DISTORTION
Claim is reworded in way that shifts its original meaning
27 (39.7%)
FABRICATION
Claim comes from the L.M's parametric memory instead of input claims
20 (29.4%)
CONFLATION
Claim is merged from two or more distinct input claims
12 (17.6%)
VERIFIER
Claim is supported but mis- flisgf by the verifier
9 (13.3%)
Table 31: PIC performance on BioASQ Task B [50], along with other 8B LM baselines. This the
partial PIC setting. Best values in bold.
Model
Precision
Perf. Prec.
LLaMa 3.1 8B INST.
80.9
22.4
TULu 3 8B
86.4
21.7
MINISTRAL INSTRUCT
88.9
49.7
HERMES 8B
81.3
26.6
PIC-LM 8B
97.5
57.0
Table 32:
PIC-LM training across model sizes.
Model Setting
Avg. Fl
Avg. Perf. F1
Avg. Prec.
Avg. Perf. Prec.
Llama 3.2 1B INST.
54.8
1.5
55.0
18.3
PIC-LM
75.5
5.1
76.3
30.0
LLAMA 3.2 INST.
54.8
1.6
63.0
21.4
PIC-LM 3B
87.0
17.8
82.4
47.7
LLAMA 3.3 70B INST.
82.5
13.1
74.3
49.5
PIC-LM 70B (QLoRA)
93.2
33.2
95.9
51.8
56

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Table 33; End-to-end example for our factuality pipeline.
Step 1
Draft instruction
Name some politicians born in Phoenix, Arizona.
Return your oulput in numbered list form (enumerated as '1. <entity I>\n 2. <entity 2>\n 3...", and with no other explanation additional information.
Llama 3.3 70B Instruct Response
1. John McCain
2. Barry Goldwater
3. Kyrsten Sinema
Step 2 Draft verification questions
1. Where the politician John McCain born?
2. Where was the politician Barry Goldwater born?
3. Where was the politician Kyrsten Sinema born?
Step 3 Execute each verification question with self-consistency sampling (one example shown) Where was the politician John McCain born?
Llama 3.3 70B Instruct Response
1. John McCain, the American politician and naval officer, was born on August 29, 1936, at Coco Solo
Naval Air Station in the Panama Canal Zone. + FALSE
2. John McCain
born Phoemix, Arizona. - TRUE
3. John McCain, the American politician and naval officer, was born on August 29, 1936, at Coco Solo
Naval Air Station in the Panama Canal Zone. + FALSE
4. John McCain, the American politician who served as United States Senator from Arizona from 1987 until his death in 2018, was born August 29, 1936, Coco Solo Naval Air Station in the Panama Canal Zone. + FALSE
5. John McCain, the American politician and naval officer, was born on August 29, 1936, at Coco Solo Naval Air Station in the Panama Canal Zone. - FALSE
Assemble verified claim list (TRUE > 3) as input for final response generation Your task is to complete the instruction. Ground your response the given context, while also answering question. Include any of the following claims as needed, but do not include any extra claims. Who are some politicians born in Phoenix, Arizona? Claims: 1. Barry Goldwater is politician born in Phoenix. Arizona.
Step 4
Instruction:
Llama 3.1 Instruct Response: Barry Goldwater was indeed in Phoenix, Arizona. He served as U.S. Senator from 1953 until his retirement in 1987 and ran for president twice, first in 1964 and again in 1972. His strong conservative views made him influential figure in American politics during that time period. Additionally, John McCain, another prominent political figure, was also born in Coco Solo, Panama Canal Zone raised Hanoi, Vietnam and later moved Phoenix, Arizona where he attended high school at the North High School before attending college. However, it's worth noting that neither of these individuals were actually born in Phocnix proper (Goldwater in Morrison Institute for Public Policy) both parents may have been residents there when they like to provide more information about other notable figures who were truly born within city limits: One such individual is Sandra Day Connor, who was born in Paso, Texas but grew up in Paso County, then later moved with her family to Duncan, Arizona: however she did attend law school in San Francisco California after graduating from Stanford University. Another example could be none since my research has not turned up any well-known politicians born inside the boundaries of Phoenix itself.
but rather
conceived. Therefore, I would
Llama 3.3 70B Instruct Response: Some the notable politicians born Phoenix include Barry Goldwater and Sandra Day O' C'Conor. Both have had significant careers public service with Goldwater serving as U.S. Senator from Arizona for five terms and O' O'Connor being appointed as an Associate Justice of the Supreme Court of the United States by President Ronald Reagan, becoming the first woman to hold that position.
PIC-LM Response: From given claims, Barry Goldwater politician born in Phoenix, Arizona.
57

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Table 34: RAG ablations on ASQA in standard (948 samples) and oracle (885 samples) regimes, reported average EM + 95% bootstrapped Cls. PASSAGE-LEVEL CONTEXT: context is passed in the original form of passages instead of verifiable claims; No KERANKING: we use the top-5 retrieved context (without greedy reranking) from Gao al. (27]. Best values in bold.
PASSAGE-LEVEL, CONTEXT
No RERANKING
Setting
Standard EM Oracle EM
Standard EM
Oracle EM
LLAMA 3.1 8B INST.
57.5
61.2
40.4
48.6
Tunu 8B
38.6
41.1 119
31.9 2..0
38.3 2.1
MINISTRAL 8B INST.
32.0
34.0 1.8
23.7 2..
28.5 2.0
HERMES 8B
53.4 2.1
56.7 2:0.0
41.0 #:2
49.2 2:2
PIC-LM 8B
59.7 :
63.7 23-1
46.4 2.3
56.0 1
Table 35: Qualitative examples from ASQA, a long- QA dataset featuring ambiguous questions that require disambiguation using multiple candidate answers. Exact string match answers highlighted in red.
Instruction
Llama 3.1 8B Inst.
PIC-LM 8B
When was the bellagio las vegas built? Note that there are multiple possible answers; please return all
The Bellagio in Las Vegas built and officially opened on Oc- tober 15, 1998. (EM: 0.5)
The Bellagio in Las Vegas structed beginning in May 1996 and it opened on October 15, 1998. (EM: 1.0)
of them.
Context...
Table 36: Text style transfer on the factuality pipeline samples. We consider six style transformations: positive, negative, polite, opinionated, humorous, and satirical. For each setting, we report (factual precision, winrate over the baseline without style constraints), with best numbers in bold.
Setting
POsiTIVE
NEGATIVE
POLITE
OPINIONATED
HUMOROUS
SATIRICAL
LLAMA 3.1 8B INST.
82.2/ 94.3
79.7 61.4
72.9 94.3
76,0
76.4 / 98.9
83.9 100.0
MINISTRAL 8B INST.
89.0 93.2
59.7 96.6
90.2 / 100.0
87.4/97.7
86.5 / 98.9
82.1 / 100.0
TULu
81.5 95.5
72.7
80.5 100.0
74.7 100.0
76.8 100.0
77.8 / 100.0
PIC-LM 8B
89.4 /98.9
89.8 96.6
88.1 100.0
89.7 95.5
89.2 100.0
85.3 / 14.3
58

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Table 37: Text style transformations (opinionated, humorous, negative) from the pipeline birthplace task for Llama 3.1 Instruct and PIC-LM 8B.
Instruction
Llama 3.1 8B Inst.
PIC-LM 8B
Name some entrepreneurs born in Seattle, Washington in a(n) opin- ionated way.
I'm not aware of any notable entrepreneurs who were actually born in Scattle, but one person that comes to mind as being closely as- sociated with the city is Paul Allen. Нe was indeed an entrepreneur, co-founding Microsoft alongside Bill Gates, which revolutionized personal computing and changed the world forever! However, it's worth noting that wasn't techni- cally born there; rather, his family moved to Seattle when he was just five years old. Nonetheless, Seat- tle will always hold special place in his heart (and legacy), making him arguably its most famous son.
Paul Allen is a entrepreneur horn in Seattle, Washington. confidently say that he an in- dividual who possesses great en- trepreneurial spirit.
Verified: Paul Allen
Name some professional athletes born in Honolulu, Hawaii in a(n) humorous way.
Ahah! You want to know about pro athletes from the 808 (that's Hawaiaan for "Honolulu")" Well, let me tell you we' ve got some talented folks who were born with sunshine in their eyes and surf- boards under their feet!
Carissa Moore and Michelle Wie two professional athletes born in Honolulu, Hawaii. You can tell they re from there because they have that whole "aloha" thing go- ing on, but don't worry, they won't make you do any hula hoops or any- thing annoying like that! They'll '1l just give you those intense stare- downs that say. "I m focused on winning could kill!"
Verified: Carissa Moore, Michelle Wie
There's 's the queen surfing her- self, Carissa Moore! Born and raised in Honolulu, this girl can ride waves like they her personal playground. She's won so many competitions that I'm pretty sure she has a trophy cabinet bigger than my grandma's 's collection of floral shirts!
Name some professional athletes born in Baltimore, Maryland in a(n) negative way.
T am unable to verify any notable or well-known professional athletes who were actually born in Balti- more, Maryland. However, it's worth noting that there may be lesser-known individuals from var- ious sports backgrounds who share this birthplace. Notable examples such as Juan Dixon and Michael Phelps have been mentioned pre- viously but they do not meet the criteria their claim was made about being "born" rather than having grown up or resided in Baltimore for an extended period. Therefore, based solely avail- able information regarding promi- nent figures with direct ties Bal- timore through birth, my search has yielded nothing conclusive at present.
Two professional athletes horn in Baltimore, Maryland are Juan Dixon and Michael Phelps. How- ever. would like to point out both of these individuals are not ideal candidates since they do not represent the best that our city has offer. fact, if we were look for the crème la crème of Bal- timore athletes, we would likely have to search elsewhere as nei- ther Dixon Phelps embodies elite level of performance that we should expect from a top- -iet athlete.
Verified: Juan Dixon, Michael Phelps
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Reviews:
[{"review_id": "Xg5CR1utXR", "reviewer": "Reviewer_Dux5", "summary": "This paper focuses on the intrinsic hallucination and introduces Precise Information Control (PIC), a new task formulation for evaluating and improving language models’ ability to generate long-form text with no intrinsic hallucinations, which is strictly grounded in provided input claims without introducing unsupported information. There are two proposed evaluation settings: full PIC and partial PIC. They also develop PIC-bench, a benchmark with 8 long-form generation tasks, and introduce PIC-LM, an 8B parameter model trained through a two-stage approach combining supervised fine-tuning with preference optimization using a novel weakly-supervised data construction method. \nThey demonstrate that even SOTA models have intrinsic hallucination in over 70% of outputs, while PIC-LM achieves better performance in both settings.", "questions": "See weaknesses.", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Well-presented, good motivation: I like the motivation of the paper, PIC-Bench could become standard benchmark for intrinsic hallucination research. In real world, medical, legal, and journalistic domains urgently require controllable information generation. Also, intrinsic hallucination has been understudied compared to extrinsic hallucination; this work can serve as a foundation for future intrinsic hallucination research.\n- Precise framework for a more fine-grained task: this paper transforms the vague binary judgment evaluation into precise claim-level constraints, also why “verifiable claim constraints” rather than sentence and atomic is well supported. The authors define clear constraints through  rigorous set relationships, and also provide computable and optimizable objective functions. \n- Well-designed Benchmark: the authors provide 8 different long-term generation tasks, and also two different settings for different real-world applications. They also provide a complete and comprehensive technical implementation details. \n\nWeaknesses:\n- I find the evaluation pipeline depends a lot on the LLM, e.g. GPT 4o, if the LLM used has some biases in claim extraction and verification accuracy, then this could result in failure to generalize to evaluation with different LLM judges.", "was_revised": true, "final_justification": "My questions are answered. However I'm not an expert in this area, I cannot be confident enough to provide helpful reviews.", "rebuttal": "We thank reviewer **Dux5** for their time and feedback! We are encouraged that **Dux5** finds our paper “well-presented [with] good motivation,” that our PIC framework “can serve as a foundation for future intrinsic hallucination research” and is “precise…for a more fine-grained task,” and that our benchmark is “well-designed…[with] complete and comprehensive technical implementation details.”  \n\nResponding to the reviewer’s comment below:\n\n**\"[T]he evaluation pipeline depends a lot on the LLM…[which] could result in failure to generalize to evaluation with different LLM judges.\"**\n\nWe thank the reviewer for raising this important point. We acknowledge that evaluations that rely on a specific LLM (in our case, GPT-4o mini) may be susceptible to model-specific biases that affect generalizability. We address this concern along two fronts, with new experiments:\n\nFirst, we explore changing the LLM backbone to GPT-4.1 and Claude 3.5 Sonnet when evaluating generations from the following baselines: Llama 3.1 8B Inst., PIC-LM 8B, and GPT-4o. We report the effect of different LLM judges on avg. PIC-Bench F1/precision. The results are as follows:  \n\n|       **LLM Judge**        | **Llama 3.1 8B Inst.** |    **PIC-LM 8B**     |     **GPT-4o**     |\n|:--------------------------:|:----------------------:|:--------------------:|:------------------:|\n| GPT-4o mini (in our paper) |      69.1 / 73.6       |      89.9 / 91.2     |     90.5 / 90.2     |\n| GPT-4.1                    |      68.6 / 73.6       |      87.5 / 88.1     |     87.6 / 86.0     |\n| Claude 3.5 Sonnet          |      70.2 / 74.5       |      88.2 / 90.0     |     89.6 / 89.3     |\n\nWe find that PIC scores are largely judge-agnostic, with GPT-4.1 behaving slightly stricter than the other two judges. Across judges, PIC-LM 8B consistently achieves the highest precision (partial PIC metric), while GPT-4o attains the highest F1 (full PIC metric). \n\nIn practice, like most automatic LLM-based evaluation methods, we fix to one judge model for a fair comparison. Furthermore, note that employing GPT-4o mini as the LLM backbone costs roughly **7 USD** for a full PIC-Bench evaluation run; based on API costs, GPT-4.1 costs roughly 13x more, and Claude 3.5 Sonnet is 20x more.  \n\nSecond, our LLM judge of choice (GPT4o-mini) shows high agreement with human annotation. The LLM is invoked at two stages of our evaluation pipeline: **claim extraction** (decomposing text into verifiable claims) and **claim verification** (checking that a claim is supported by a set of verifiable claims). We conduct a pilot human evaluation experiment to assess agreement across PIC tasks and 3 baseline models (Llama 3.1 8B Instruct, PIC-LM 8B, and GPT-4o). We use 3 human annotators per component. \n\n\n## Claim Extraction Human Evaluation\nFor claim extraction evaluation, we sample 5 generations per PIC task and baseline model, leading to 120 samples in total. For each sample, we show the annotator the generated output and an extracted claim from that output. \n\nRecall that for claim extraction, the LLM is prompted to extract high-quality, verifiable claims from a source task. Here we ask the annotator to rate the extracted claim on a 3-point ordinal scale (0-2, 0 being the best) along the following dimensions: **faithfulness** (0: definitely supported, 1: partially supported, 2: not supported), **decontextualization** (0: self-contained, 1: ambiguous, 2: context-dependent), and **quality** (0: fluent, 1: minor readability issues, 2: hard to read). \n\nWe report (i) LLM-human agreement (the average fraction of annotator ratings equal to the best score (0)  for the LLM-extracted claims), and (ii) exact inter-annotator agreement (the fraction of items where all raters chose the same label). \n\n\n| **Dimension / Metric**    |     **LLM–Human Agreement (%)**     |     **Exact Inter-Annotator Agreement (%)**     |\n|:-------------------------:|:-----------------------------------:|:------------------------------------------------:|\n| Faithfulness              |                97.5                 |                      94.1                        |\n| Decontextualization       |                89.6                 |                      76.5                        |\n| Quality                   |                99.7                 |                      99.2                        |\n\n\nWe observe fairly strong LLM-human agreement (and substantial exact inter-annotator agreement) across all three axes, which confirms that GPT-4o mini is able to correctly extract high-quality, verifiable claims. \n\n## Claim Verification Human Evaluation\nFor claim verification evaluation, we sample 3 generations per PIC task and baseline, leading to 72 generations in total. For each sample, we provide the list of input claims, and a response claim that the LLM-based claim verifier has marked as either supported or unsupported. We ask the annotators, who are blind to model identity and the verifier’s label, to judge if the response claim is supported or unsupported by the input claims. \n\nWe found high average human agreement with the LLM (**81.0%**),  and substantial inter-annotator agreement (Fleiss’ Kappa of **0.694**).\n\nTaken together, these results give confidence that our evaluation pipeline (using GPT-4o mini) is producing evaluations that are broadly consistent with human judgment. Finally, we emphasize that our PIC evaluation design is flexible and judge-agnostic: in practice, it’s easy to swap to a different LLM depending on one’s use case."}, {"review_id": "8MIXQVPkhC", "reviewer": "Reviewer_pcWR", "summary": "Precise Information Control (PIC) is a new generation task that eliminates intrinsic hallucinations by demanding outputs containing exactly and only a supplied list of factual claims. The authors construct PIC-Bench, eight datasets spanning biographies, QA, and news summaries in both “full” (all claims required) and “partial” (only relevant claims) modes, and show that even GPT-4 and Claude add or omit claims in over 70 % of cases. They fine-tune an 8B Llama-3 model with SFT + DPO to obtain PIC-LM, which delivers higher factual fidelity while preserving general instruction-following skills (judged by Promotheus). They also show that plugging PIC-LM into retrieval-augmented QA and self-verification pipelines yields noticeably more trustworthy answers.", "questions": "* Consider providing a breakdown of remaining errors (missing vs. unsupported claims, topical patterns) and discuss how those insights can guide future mitigation strategies.  \n* In the partial PIC setting, only a subset of provided claims should be used. It would help to know how the “relevant” subset is determined for evaluation. The paper could be more explicit on whether this is based on the original reference output or some information retrieval heuristic. For example, if multiple claims in the context could have been relevant but the model was penalized for including them (because the reference didn’t), that might unfairly penalize the model.  \n* How do the PIC-LM outputs do in terms of coherence and style, beyond just containing the required facts? It would be great to include a small qualitative analysis or human evaluation of output quality.   \n* By design, PIC penalizes any extra information not present in the input, even if that information is factually correct. This stringent criterion is appropriate for measuring hallucination, but it could be argued that in some applications a harmless additional detail (especially if true) might not be a failure. Could the authors provide more discussion on this nuance?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n* The paper introduces a distinct and granular formulation of the hallucination problem. Instead of treating intrinsic hallucination as a binary phenomenon, PIC enforces and measures faithfulness at the level of individual factual claims. This per-claim control is a novel perspective. It goes beyond prior work by requiring exact inclusion/exclusion of specific information units, a stricter criterion than traditional factuality metrics.  \n* PIC-Bench covers eight tasks spanning different domains and formats. These include: generating Wikipedia-style biographies (EntityBios, PopBios), open-ended Q\\&A from Reddit (ELI5, AskHistorians), expert-curated Q\\&A (ExpertQA), abstractive news summarization (XSUM), and a long-document grounded generation task (FACTS).  \n* An 8B PIC-LM lifts full-PIC F1 from 69.1% to 89.9% and delivers strong precision improvements in downstream QA/verification pipelines.\n\nWeaknesses\n\n* The fine-tuned PIC-LM is specialized to follow given facts religiously. A possible weakness is how this specialization affects its general language abilities or flexibility. The paper includes an alignment evaluation (“Prometheus score”) suggesting only a minor drop in general helpfulness, but a more extensive evaluation on non-PIC tasks would strengthen confidence in PIC-LM’s general usability.  \n* PIC penalizes any additional (even correct) information; the paper lacks discussion or ablation on whether such rigid constraints harm user value in contexts where minor extra facts are tolerable.  \n* The evaluation focuses almost entirely on factual inclusion metrics (F1, precision of claims) and Prometheus scores. There is little assessment of the linguistic quality or user-perceived quality of the outputs.   \n* The research focuses on an 8B-parameter model for fine-tuning. While this is pragmatic, it remains unclear how well this method would scale to bigger or smaller models.", "was_revised": true, "final_justification": "The authors have thoroughly addressed my concerns from the original review, providing several new results that will be valuable additions to the paper. Accordingly, I have decided to raise my score.", "rebuttal": "We thank reviewer **pcWR** for their time and feedback! It is encouraging that the reviewer finds our PIC framework to bring “a novel perspective” by enforcing per-claim control, “[going] beyond prior work” and that our PIC-LM “delivers strong precision improvements” in concrete downstream use cases. We address their questions and suggestions below: \n\n**“PIC-LM is specialized to follow given facts religiously…[which may] affect its general language abilities or flexibility. [A] more extensive evaluation on non-PIC tasks would strengthen confidence in PIC-LM’s general usability.”** \n\nWe did not evaluate PIC-LM on general language benchmarks as it’s not meant to be a standalone LM, but rather a highly faithful module within larger systems for grounded generation (Sect. 6). For example, using PIC-LM as a drop-in replacement in RAG or chain-of-verification pipelines boosts end-task factual accuracy (+**14%** on ASQA, +**21%** on Birthplaces, +**6.9%** on QamPARI). \n\nAlthough PIC-LM is a specialized model (its alignment process is solely focused on PIC), for transparency, we report new results on standard benchmarks (MMLU and ARC-C using OLMES [1]; GSM8K using LM-Harness [2]; and AlpacaEval 2 [3]):\n\n| **Benchmark**                                      | **Llama 3.1 8B Inst.** | **PIC-LM 8B** |\n|---------------------------------------------------|:----------------------:|:-------------:|\n| 5-shot MMLU Acc. (Raw Completion)                 |          43.6          |   **44.2**    |\n| ARC-Challenge Acc. (Raw Completion)               |          55.6          |   **58.7**    |\n| 5-shot GSM8K Exact Match                          |        **75.7**        |     69.3      |\n| AlpacaEval 2 Length-Controlled Win Rate           |        **20.9**        |     17.1      |\n\n\nWe emphasize that PIC-LM is an initial model to tackle PIC, which still remains an open problem. For example, it is possible to instill general-purpose LMs with better PIC abilities by mixing in PIC-formatted data during post-training. We leave this direction to future work, and will include the above results in our paper with a more in-depth discussion. \n\n**“PIC penalizes any additional (even correct) information; the paper lacks discussion or ablation on whether such rigid constraints harm user value in contexts where minor extra facts are tolerable…could the authors provide more discussion on this nuance?”**\n\nWe acknowledge that PIC’s strict grounding requirement isn’t appropriate for every scenario (e.g., creative storytelling, open-ended reasoning). Rather, PIC is built around user-specified control: if the user explicitly instructs the model to only ground its response on given claims, the LM should comply, even if this means forgoing extra correct details. Enforcing these constraints is necessary in fast-moving, high-stakes domains (e.g., scientific consensus on infectious diseases), where plausible but unverified information pose serious risks.  We will incorporate these points into the discussion in our revision. \n\n**“There is little assessment of the linguistic quality or user-perceived quality of [PIC evaluation] outputs…How do the PIC-LM outputs do in terms of coherence and style, beyond…required facts? …Include a small qualitative analysis or human evaluation of output quality.”** \n\nThanks for the suggestion! We ran a new small-scale qualitative analysis by sampling 40 PIC-Bench instructions and comparing responses from Llama 3.1 8B Instruct and PIC-LM 8B along two dimensions: coherence and fluency. Following prior work [5], we define coherence as the degree to which  the ideas in the text flow together logically (i.e., whether the sentences are on-topic, and follow a clear progression of thought). We define fluency to be how grammatically correct, smooth, and natural the text is. \n\nFor each output pair, we indicated a preference for Llama 3.1 8B Inst., PIC-LM 8B, or a tie along each dimension. All outputs were evaluated blindly in shuffled order, without revealing model identity, to reduce annotation bias. The results are as follows: \n\n|                         | **Prefer Llama 3.1 8B Inst.** | **Tie** | **Prefer PIC-LM 8B** |\n|-------------------------|:-----------------------------:|:-------:|:--------------------:|\n| **Coherence**           |           25%               |  27.5%  |      **47.5%**       |\n| **Fluency**             |            2.5%               |  **72.5%**|        25%           |\n\n\nThese results suggested that PIC-LM 8B outputs tend to be more coherent than Llama 3.1 8B Inst. Qualitatively, we find that PIC-LM generations are more concise and factual, whereas Llama generations are more verbose. Both model generations are similarly fluent. we'll include this experiment in our revision, and provide more qualitative examples of PIC-LM generation. \nNote that PIC-LM can also be steered along stylistic dimensions while preserving claim grounding. We demonstrate this capability in a style transfer experiment in Appendix D.2.1.\n\n**“It remains unclear how well [PIC-LM training] would scale to bigger or smaller models.”** \n\nWe ran new PIC-LM training experiments starting from 1B, 3B, and 70B Llama 3 Instruct models, and found strong PIC-Bench improvements in all settings. Note that due to computational constraints, we trained the 70B PIC-LM with QLoRA fine-tuning. Please see the PIC-Bench results below: \n\n| **Model Setting** | **Avg. F1** | **Avg. Perf. F1** | **Avg. Prec.** | **Avg. Perf. Prec.** |\n|-------------------------------------|:-----------:|:-----------------:|:--------------:|:---------------------:|\n| Llama 3.2 1B Inst.                  |    54.8     |       1.5         |     55.0       |         18.3          |\n| **PIC-LM 1B**                       |  **75.5**   |     **5.1**       |   **76.3**     |       **30.0**        |\n| Llama 3.2 3B Inst.                  |    54.8     |       1.6         |     63.0       |         21.4          |\n| **PIC-LM 3B**                       |  **87.0**   |    **17.8**       |   **82.4**     |       **47.7**        |\n| Llama 3.3 70B Inst.                 |    82.5     |      13.1         |     74.3       |         49.5          |\n| **PIC-LM 70B (QLoRA)**             |  **93.2**   |    **33.2**       |   **95.9**     |       **51.8**        |\n\n\n**“Consider providing a breakdown of remaining errors (missing vs. unsupported claims, topical patterns) and discuss how those insights can guide future mitigation strategies.”**\n\nThanks for the suggestion! We manually examined **40** PIC-LM generations on PIC-Bench with imperfect scores (5 samples per PIC evaluation task). This sample has **1165** output claims in total, of which **68** response claims are marked as unsupported (affecting precision), and **63** input claims are marked as missing (affecting recall for the full PIC setting). \n\nWe provide a categorical error breakdown of the 68 **unsupported** response errors below:\n\n| **Error Type** | **Explanation**                                                        | **Count (Percentage)** |\n|----------------|------------------------------------------------------------------------|-------------------------|\n| Distortion     | Claim is reworded in a way that shifts its original meaning            | 27 (39.7%)              |\n| Fabrication    | Claim comes from the LM’s parametric memory instead of input claims    | 20 (29.4%)              |\n| Conflation     | Claim is merged from two or more distinct input claims                 | 12 (17.6%)              |\n| Verifier       | Claim is supported but mis-flagged by the verifier                     | 9 (13.3%)               |\n\n Among the 63 **missing** claims, we observe no obvious qualitative differences, but a mild head-bias: **27%** of drops occur within the first quarter of the list (vs. **22%** in the final quarter), indicating that earlier claims are slightly more likely to be omitted, and corroborating the “lost-in-the-middle” context phenomenon. We'll add these error analyses to our revision. \nFor mitigation strategies, we believe it would be beneficial to explore improved data selection during training, such as incorporating hard negatives with subtle distortion and fabrication errors. Additionally, adopting techniques from long-context modeling (e.g., attention re-weighting, recurrence, or segment-aware encodings) may help address position-based errors, such as the observed head-bias in missing claims.\n\n\n**“In the partial PIC setting, only a subset of provided claims should be used. It would help to know how the “relevant” subset is determined for evaluation.”** \n\nIn the partial PIC setting, it is up to the LM to select the relevant subset from the input claims. PIC does not measure the quality of the answer, but just ensures that every claim in the answer is fully grounded. PIC is reference-free in that the output isn’t compared against a given gold answer.\n\nTo measure actual generation quality  (e.g., if the response adequately satisfies the task instruction), we pair PIC evaluation with Prometheus, an LLM-based judge that scores outputs along a user-defined rubric and shows high agreement with human evaluations. As our PIC evaluation framework provides well-formed claims, we find that our Prometheus scores are relatively high (see Table 4).  \n\n\n\nCitations:\n\n[1] Gu et al., 2025. OLMES: A Standard for Language Model Evaluations\n\n[2] Biderman et al., 2024. Lessons from the Trenches on Reproducible Evaluation of Language Models\n\n[3] Dubois et al., 2024. Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators\n\n[4] Liu et al., 2023. Lost in the Middle: How Language Models Use Long Contexts \n\n[5] Li et al., 2023. Contrastive Decoding: Open-ended Text Generation as Optimization"}, {"review_id": "YUhUoewjx6", "reviewer": "Reviewer_UZLz", "summary": "The paper introduces a task called Precise Information Control (PIC) in an attempt to measure the ability of LMs to generate long-form text grounded in a set of provided input statements, i.e. without intrinsic hallucinations. In this task, the LM is provided with a set of short verifiable claims and an instruction to generate the long form output in one of 2 settings - a *full setting* in which the model is expected to utilize all provided claims in the generation, and a *partial setting* in which the model can choose to select relevant claims to be included. A verifiable claim extractor from past work (VeriScore, Song et. al. 2024) is utilized for extracting claims from summarization, biography generation & QA datasets to construct the input data, which results in a set of 8 datasets curated in the PIC setting and (expected to be) released as a benchmark for this task. The claim extractor is also applied to the generated response in order to compute support with supplied input claims. \nApart from off the shelf LLMs, a Llama 3.1 8B Instruct model is fine tuned using a) SFT b) DPO and compared against baselines. The model finetuned with DPO shows better performance than most instruction tuned & reasoning models for both full & partial settings.", "questions": "1. Given that the LLMs are sensitive to the instruction prompts, were there experiments conducted by varying the prompts used for the problem for the full & partial settings? Specific instruction prompts are likely to garner better performance from the models. \n2. Could you elaborate on how the gold response was obtained (line 111)?\n3. A few lines on the model used to compute support for eval (line 115) would be helpful. Support is typically posed as an NLI task in these cases, but it would help to understand what was used here. \n4. Which instruction-following model is referred to in line 184?\n5. It is good to see the DPO-finetuned Llama 8B model to be at par/outperform other baselines in Tables 2 & 3. However, it's curious to see the SFT model degrade in performance w.r.t the same base model in F1 for AskH, ELI5, ExpertQA & FACTS. Do you have any thoughts on why that might be the case?", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Several types of datasets have been covered for this problem, i.e. summarization, biography generation & QA, which is commendable. \n2. The paper is fairly well-written and contains exhaustive experiments in several directions, e.g. analyses on instruction following capabilities of the LM, comparison with RAG settings, etc.\n\nWeaknesses:\n1. Precise Information Control has been presented as a new task. However, it is very similar to RAG & attributable generation (without the need for explicit citations) as the paper also states (Sec 6, 7), where the generated response is required to be grounded in provided source references. Both the task setting and the evaluation processes are similar between the two. The authors attempt to address this by stating that the full PIC setting requires inclusion of all input claims, but that does not suffice in terms of novelty since this would be achievable in the attributable generation setting by modifying the input instruction prompt to direct the LM to include all source refs as well. \n2. The task expects claim extraction as a necessary step to be conducted from typically noisy input source material that is generally present in RAG settings. This creates a large dependency on efficacy of the claim extractor model, and assumes that only relevant claims will make their way into the input. This seems to be an unrealistic setting to expect in the real-world.", "was_revised": true, "final_justification": "Updating my score by 1 based on the rebuttal, particularly the details provided on comparing PIC with RAG, including the new experimental results.", "rebuttal": "We thank reviewer **UZLz** for the constructive feedback. We are encouraged that the reviewer finds the breadth of datasets covered in PIC-Bench to be “commendable,” and that our paper is “well-written [with] exhaustive experiments in several directions.” Responding point-by-point to their comments:\n\n**“[PIC] is very similar to RAG & attributable generation (without the need for explicit citations)...[full PIC] would be achievable in the attributable generation setting by modifying the input instruction prompt to direct the LM to include all source refs as well.”**\n\nAlthough PIC shares surface similarities with RAG and attribution, this framework is fundamentally about information control—ensuring that every output claim is traceable to user-provided input. This goal differs from that of RAG and attribution generation, both of which primarily aim to improve factuality. We show that PIC is surprisingly difficult for LMs; when provided with exactly which claims to include, even frontier LMs may drop, distort, or fabricate content. This fundamental control problem motivates a dedicated evaluation framework, benchmark, and training techniques.\n\nWhile PIC, RAG, and attributable generation all require grounded responses, we note several key differences. First, PIC enforces full coverage of all input claims, while RAG allows for information omission if final answers are correct. Second, attribution systems often work at the sentence or passage level, while PIC operates at verifiable claim level, enabling a finer-grained detection of distortion, fabrication, and conflation. Prompting an LM to “include all sources” can only approximate this in a best-effort way, and cannot guarantee coverage of every input claim. Lastly, PIC has broad task coverage. While RAG and attribution generation primarily focus on QA, PIC is more universally applicable to a range of generative tasks.\n\nImprovements on PIC may also benefit related tasks. We demonstrate strong factuality gains in our RAG experiment (Section 6.1), and leave attribution generation to future work. Notably, PIC-LM outperforms Self-Cite 8B, a strong attribution generation LM, across both partial and full PIC settings (our RAG and self-verification pipeline case studies).\n\n**“[PIC] expects claim extraction as a necessary step to be conducted from typically noisy input source material…This creates a large dependency on [the] efficacy of the claim extractor model, and assumes that only relevant claims will make their way into the input…[which seems] unrealistic.”**\n\nThanks for pointing this out. For PIC-Bench evaluation and our RAG experiments, we use a claim extractor from [6] that decomposes a given source text into its constituent verifiable claims. We emphasize that this extractor is not prompt-aware, and does not filter claims based on user instruction. \n\nWe would also like to correct the assumption that PIC assumes that only relevant claims will be in the input. In partial PIC (which is used for RAG), the LM is fed a complete claim set that may contain noisy, irrelevant, or contradictory claims. The LM must select *a relevant subset of claims* to ground its output, without adding any unsupported claims.  Concretely, it's the LM, not the claim extractor, that must decide which claims to incorporate.\n\nWe ran RAG experiments with retrieved passages processed as claims as PIC-LM is trained with this format. However, we conduct a new RAG ablation, where we *omit the claim extraction step entirely* and instead pass in raw retrieved passages to PIC-LM 8B (“PIC-LM 8B (Psg.-level)”). This setup corresponds to standard in-context RAG, and simulates noisy input without explicit claim structuring. Despite being trained only on structured claims, PIC-LM generalizes well to this unstructured setting and still outperforms Llama 3.1 8B Inst. by a significant margin (**59.7** vs. **52.3** EM) on ASQA:\n\n| **Baseline**                  | **Exact Match** |\n|------------------------------|------------------|\n| Llama 3.1 8B Inst.           | 52.3             |\n| PIC-LM 8B                    | 59.4             |\n| **[New]** PIC-LM 8B (Psg.-level)       | **59.7**         |\n\nPIC-LM improves downstream RAG performance as it grounds on retrieved context more thoroughly. Please note that not all PIC use cases rely on an auxiliary claim extractor. For example, in our chain-of-verification pipeline experiments, the LM autonomously generates and self-corrects its claims to improve end-task factuality. We will incorporate these results and discussion into our revision.\n\n**“Were there experiments conducted by varying the prompts used for [PIC evaluation]?”**\n\nWe ran a new ablation experiment with stronger, more explicit prompts and found PIC-Bench to be largely prompt-insensitive. We report absolute performance changes (%)  for PIC-LM 8B and three baselines when evaluated using the original prompt (from the paper) versus an alternative prompt pair:\n- **Full PIC:** “You are an assistant that strictly adheres to given instructions. Be sure to incorporate each provided  input claim in your response, and do not introduce or omit any claims.” \n- **Partial PIC:** “You are an assistant that strictly adheres to given instructions. Respond using any subset of given claims that you find relevant, but do not introduce any new claims.”\n\n| **PIC Metric (%)** | **Llama 3 8B Inst.** | **Tulu 3 8B** | **Ministral 8B Inst.** | **PIC-LM 8B** |\n|:-------------------------------:|:--------------------:|:------------:|:----------------------:|:-------------:|\n| **Avg. F1**                     | 1.8                  | 1.1          | -2.4                   | 0.4           |\n| **Avg. Perf. F1**               | 0.4                  | 0.0          | -0.4                   | 3.2           |\n| **Avg. Prec.**                  | 0.8                  | 3.2          | 1.2                    | 0.4           |\n| **Avg. Perf. Prec.**            | -0.3                 | 0.1          | 3.0                    | 4.2           |\n\nAs shown, changes in PIC performance are relatively small across prompt variations, with most metric shifts **<4%**. These results indicate that PIC-Bench evaluations remain stable across reasonable prompt variations. We will include these results in our revision. While certain prompts may slightly improve absolute scores, we believe they are unlikely to affect the relative ranking between models, which is our primary evaluation focus.\n\n**“Could you elaborate on how the gold response was obtained (line 111)?”**\n\nThe long-form gold response $y$ comes from the instruction-following dataset, $D_{IT}$. Many off-the-shelf instruction-following datasets (e.g., Alpaca) are formatted as instruction-response pairs $(I, y)$, which can be easily converted to the PIC format $(I, C, y)$, wherein $C$ is the set of extracted verifiable claims from the response $y$. We'll clarify this point in our revision. \n\n**“A few lines on the model used to compute support for eval (line 115) would be helpful.”**\n\nThanks for the suggestion! We use an off-the-shelf claim verifier introduced by VERISCORE [6], which prompts an LLM (in our case, GPT4o-mini) to judge whether a verifiable claim is supported or unsupported by a corresponding set of evidence claims. Note that [6] has found substantial agreement between this claim verifier and human annotators; we also corroborate this across our PICBench tasks and baseline generations in our own human evaluation (please see our response to reviewer **Dux5**).\n\n**“Which instruction-following model is referred to in line 184?”**\n\nWe take **Llama 3.1 8B Instruct** as the instruction-following model used to score responses for PIC-LM preference data creation. We choose this model because in practice,  (1) it shows strong agreement with GPT-4o in our LLM-as-a-judge comparison (see Appendix C.5), and (2) as a relatively small, open LM, it enables efficient and cost-effective data selection.  \n\n**“It's curious to see the [PIC-LM] SFT model degrade in performance w.r.t the same base model in F1 for AskH, ELI5, ExpertQA & FACTS.”**\n\nWe thank the reviewer for pointing this out. AskH, ELI5, ExpertQA, and FACTS are the four long-form QA tasks on PIC-Bench, and PIC-LM SFT shows a drop in PIC performance compared to the base model Llama 3.1 8B Inst. on these tasks. We believe there are two reasons: first, there is a mismatch between the token-level cross-entropy loss used for SFT and the PIC objective, which emphasizes exact adherence to a constrained set of claims. During SFT, the model may learn to generate broadly informative responses that resemble typical QA-style outputs but deviate from strict PIC constraints, leading to improved recall but reduced precision, and thus lower F1.\n\nThe second factor is SFT training data quality. Table 20 (in Appendix C.4) shows that training only on filtered samples with high PIC scores improves both SFT and DPO performance. Since submission, we have experimented with raising the minimum PIC score threshold from **0.9** to **1.0** for all training examples except those in the biography domain; as shown below, training on more stringently filtered data improves PIC performance on long-form QA. \n\n| **Model**              | **AskH F1** | **ELI5 F1** | **ExpQA F1** | **FACTS Prec.** |\n|:----------------------:|:----------:|:----------:|:------------:|:----------------:|\n| (Former) PIC-LM SFT    |   81.0%     |   77.5%     |     70.0%     |      63.1%        |\n| (Former) PIC-LM        |   94.2%     |   92.2%     |   **87.0%**   |      89.0%        |\n| (New) PIC-LM SFT       |   87.2%     |   82.1%     |     77.3%     |      81.2%        |\n| (New) PIC-LM           | **96.0%**   | **94.1%**   |     85.6%     |    **89.8%**      |\n\nWe hope our responses have adequately addressed your concerns. Please let us know if there are any other points we can clarify. \n\nCitations: \n\n[6] Song et al., 2023. VERISCORE: Evaluating the factuality of verifiable claims in long-form text generation."}, {"review_id": "VXA5ifNKaR", "reviewer": "Reviewer_8i1m", "summary": "The manuscript proposes a novel task, \"Precise Information Control\" (PIC), which involves long-form generation that includes all or part of the suggested claims.\nBy adapting existing language generation benchmarks, the authors introduce PIC-Bench, a dataset consisting of (Instruction, Claims, and Gold-Response) tuples.\nBy applying SFT and DPO to this dataset, PIC-LM achieves the best or near-best performance among open-source models and demonstrates comparable results to closed-source models.\nThe authors also verify its viability in real-world scenarios, such as retrieval-augmented generation and self-verification.", "questions": "- Please provide some qualitative results analyzing questions (1) and (2) above.\n- I am curious whether using all tokens versus only the last $L$ tokens for score computation (as mentioned in line 185) yields similar results.\n- Please provide evidence of ChatGPT–Human agreement in your evaluation process.\n- Please reconsider the presentation of the POPB-CF benchmark, as it may be misleading.", "limitations": "Although the authors mention their setting—where user-provided knowledge should supersede the LM's inherent priors—in lines 317–319, its implications are not thoroughly discussed. This approach could raise ethical concerns, as it may allow individuals to manipulate the LM to generate any content. This is precisely why several guardrails have been established in the AI community. It would be better if the authors addressed these concerns in more detail.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "- **Strenght**\n  - Overall, I like this paper. The manuscript is well written. It proposes a dataset, training method, model, and its performance on real-world scenarios. The experiments are thorough and well-motivated.\n\n- **Weakness**\n  - **Presentation**\n    - Please check the definitions of intrinsic and extrinsic hallucinations. As far as I know, intrinsic hallucination refers to hallucination that can be inferred from the given context, while extrinsic hallucination refers to hallucination that cannot be inferred from the context. ([1] – Sections 2.1 and 2.2 mention that intrinsic and extrinsic hallucinations are subcategories of factual hallucination.) Consider using the terms \"faithful\" instead of \"intrinsic\" and \"factual\" instead of \"extrinsic.\" For example, in the manuscript, the POPB-CF benchmark is faithful but not factual.\n    - It would be helpful to include some qualitative results showing (1) why a perfect match is so rare, especially in the PIC-Bench-Full setting, and (2) why instruction-following performance decreases in Table 4 after fine-tuning.\n  - **Experiment**\n    - I'm not fully convinced why XSUM data is used, as it is \"extreme summarization\" in a single sentence, as the name implies. Doesn't this conflict with the paper's focus on \"long-form text generation\"?\n    - Why did you sample the $y_{perturb}$ from $\\theta_{SFT}$, not superior models? It's a bit worrisome as $\\theta_{SFT}$'s capacity does not seem good from your results.\n    - I'm also not convinced by the strategy of using the last $L$ tokens for score computation, as described in lines 184–189. The authors mention that this approach is due to efficient computation and length bias, but couldn't the length bias be mitigated by using a different denominator?\n    - I read in the appendix, line 1259, that the train and test sets do not overlap. However, it would be better to ensure that the dataset does not have any overlap between the training set and the test benchmark. In other words, it would be better not to use FACTS and biography generation datasets for training. Since the training data uses the same (or similar) test instructions during the training procedure, this would definitely affect the final score, unlike other non-fine-tuned models.\n    - It would be helpful to compute the ChatGPT–Human agreement score, as the evaluation process heavily depends on ChatGPT's ability to extract and verify claims, at least for a few examples.\n  - **Misc.**\n    - It would be better to exclude POPB-CF from the average calculation, as this benchmark directly conflicts with some models' instructions to be factual. From Tables 2 and 4, the main source of the average score gap appears to come from this benchmark, which does not seem fair. (I'm also not sure whether this is an appropriate benchmark, as it could raise certain ethical issues.)\n\n- **Typos**\n  - Line 213: \"... 8B open-weight baseline (Llama 3.3 70B ...\"\n  - Check bold in Table 6 (Ministral 8B - QAMParI - Prec.)\n\n**References**\n[1] On Faithfulness and Factuality in Abstractive Summarization, ACL 2020", "was_revised": true, "final_justification": "Please check the additional comments.", "rebuttal": "We thank reviewer **8i1m** for the thoughtful assessment! We are encouraged that they like this paper, and that they find the manuscript “well-written,” and the experiments “thorough and well-motivated.” Thanks for catching the typos as well! Responding point-by-point:\n\n*“Check the definitions of intrinsic and extrinsic hallucinations…consider using the terms \"faithful\" instead of \"intrinsic\" and \"factual\" instead of \"extrinsic.”*\n\nWe agree that intrinsic and extrinsic hallucination have particular meanings in prior work [7,8]: (i) intrinsic denotes outputs that contradict the source, and (ii) extrinsic denotes outputs that cannot be verified from the source (and may or may not be true in reality).\n\nTo avoid confusion and align with reviewer suggestions, we'll revise our terminology to using “faithful” instead of “intrinsic”, and “factual” instead of “extrinsic.” This means that PIC evaluates faithfulness with respect to supplied verifiable claims; improving faithfulness also improves factuality. We'll update the text and expand the discussion in App. A.3.\n\n**“[Include] qualitative results showing (1) why a perfect match is so rare, especially in the PIC-Bench-Full setting, and (2) why instruction-following performance decreases [after SFT].”**\n\nFor (1), we find that a perfect F1 is rare due to distortion errors (where the model rewords claims in ways that distort the original meaning) and fabrication errors (where the model “fills in the blanks” by introducing unsubstantiated information). Please see the categorical error breakdown in our response to reviewer **pcWR**; we'll also include qualitative examples in our revision. We also observe that claim recall tends to be lower than precision, particularly when the sample has many claims, which harms the F1. This is a known problem, as LMs face a lost-in-the-middle effect and struggle to attend to long contexts.\n\nFor (2), we notice that instruction-following performance decreases after SFT as the LM learns to optimize for PIC at the expense of following instruction constraints, e.g., not following word counts.\n\nHere is an ExpertQA example (with a Prometheus score of 3/5). While this response provides a basic overview, it fails to abide by the pros-and-cons format required by the instruction:\n\n```\nInstruction: Give me an overview of the free granular synthesizer software with pros and cons for each option. [Claims omitted]\n\nPIC-LM SFT Output: \nHere are some options for free granular synthesizers\n1. Granulab - This program has many features including real-time audio processing\n2. Argotlunar - Another feature-rich granulator\n3. Hourglass - A good choice for those who want more control over their sound\n4. Fragment - Great for collaboration or live performance\n5. PaulXStretch - For advanced users looking to push the limits of time-stretching\nEach one has its own strengths and weaknesses so it really depends on how you plan to use them.\n```\n\n**“Doesn't [XSUM’s \"extreme summarization\"] conflict with the paper's focus on \"long-form text generation\"?”**\n\nWhile we use the data from XSUM, we do not adopt its one-sentence “extreme” summarization protocol, and instead prompt for standard summaries. We consider this a long-form generation task as we could extract a sufficient number of verifiable output claims; e.g., the PIC XSUM task has 30.6 avg. input claims, and PIC-LM 8B responses average around 21.8 claims. We’ll clarify the misleading task name in the revision.\n\n**“Why did you sample the y_perturb from θ_SFT, not superior models?”**\n\nWe sampled $y_{perturb}$ using $θ_{SFT}$ as it’s a cheap proxy for API-based LLMs. We reuse SFT data for preference data construction, so these perturbed responses already exhibit strong PIC behavior as $θ_{SFT}$ has seen similar samples. To empirically validate the quality of $y_{perturb}$, we randomly sample 50 full and partial PIC samples from our preference data where the perturbed sample is preferred, and find high PIC scores (**84.3%** F1 for full PIC, **86.5%** precision for partial PIC); these numbers are competitive with GPT-4o sampling. While not perfect, these signals are still valuable for preference tuning, as recent work (the Delta Learning Hypothesis [9]) suggests that the relative delta between chosen and rejected responses is more important than absolute response quality.\n\n**“[For using] the last L tokens for score computation…couldn't the length bias be mitigated by using a different denominator?”**\n\nFollowing [10], we compute the avg. per-token log-likelihood over the last L=20 tokens of each response to reduce computational cost and length bias; in this setting, the shorter response is chosen **49.0%** of the time. \n\nWe ran a new ablation where we recreate preference data by computing the per-token log probability over the entire response instead. This results in a length bias with the shorter response preferred **60.2%** of the time. Training on length-imbalanced preferences leads to worse avg. PIC performance (**91.0%** vs. **91.4%** for full, and **92.6%** vs. **93.7%** for partial PIC) and Prometheus scores (**3.89** vs. **3.92**). Thus, we retain last-L-token scoring for its balance of efficiency and performance. Alternative normalization strategies, e.g., length-aware scoring, or an adaptive L, may improve robustness; we leave this to future work.\n\n**“The train and test sets do not overlap [and] it would be better…not to use FACTS and biography generation datasets for training.”**\n\nThanks for raising this point! First, we emphasize that the PIC-LM training and PIC-Bench evaluation datasets are completely disjoint; there’s no overlap between training and test splits.\n\n Second, our evaluation is designed to measure both in-distribution (ID) and out-of-distribution (OOD) generalization. We include EntityBios and FACTS in PIC-Bench to assess ID performance for PIC-LM, which had exposure to these tasks during training. The other tasks (along with a biomedical task in App. C.7) serve as OOD evaluations, allowing us to measure performance on unseen domains. PIC-LM shows good OOD performance (**90.4%** avg. OOD F1, competitive with GPT-4o at **90.3%**). We'll make the ID/OOD distinction clear in the revision.\n\nLastly, EntityBios and FACTS contain significantly more claims (**51** and **64** on average, respectively) and are more challenging than other domains. High‑quality instruction data with long, rich contexts is scarce and expensive to curate, so these domains are instrumental to improving PIC.\n\n(Note that the ID/OOD PIC-LM numbers cited above reflect updated training results after stricter data quality control.)\n\n**“It would be helpful to compute the ChatGPT–Human agreement score.”**\n\nWe ran human evaluations; please see our response to reviewer **Dux5** for setup details. For claim extraction, the LLM-human agreement averaged 97.5%, with inter-annotator agreement at **94.1%**. For claim verification, the LLM-human agreement is **81.0%**, with substantial inter-annotator agreement (Fleiss kappa=**.694**). \n\n**“It would be better to exclude POPB-CF from the average calculation…[the] average score gap appears to come from this benchmark.”**\n\nThanks for this perspective. PopB-CF is intentionally designed as a stress test: it modifies biographical claims to conflict with models’ parametric knowledge to assess whether LMs can follow explicit user instructions, even when doing so contradicts their internal priors. This scenario simulates realistic safety-relevant settings, such as updating outdated facts or enforcing user-specific overrides. In these cases, instruction-following behavior is desirable, even if it overrides a model’s factual default.\nTo ensure a fair comparison, we agree it’s valuable to report both the average with and without PopB-CF. In our revision, we'll include both versions to make these tradeoffs transparent. Without PopB-CF, the top-3 highest avg. PIC F1s are GPT4o (**93.7%**), PIC-LM 8B (**93.0%**), and Llama 3.3 70B Inst. (**89.9%**), and PIC-LM 8B shows similar Prometheus scores to Llama 3.1 8B Inst. (**3.94** vs. **3.99**).\n\n**“[The] implications [that user-provided knowledge should supersede the LM's inherent priors] are not thoroughly discussed.”**\n\nWe appreciate the reviewer’s concern. Our work aims to make LMs more controllable by giving users finer-grained command over their generations.\n\nStronger control over model outputs may indeed exacerbate misuse. PIC-LM is more capable of generating counterfactual or harmful content if such claims are passed as input, and may override abstention behaviors instilled via RLHF. However, this tradeoff should be contextualized. Uncontrolled toxic generation, arising from model hallucinations, can be harder to detect and filter. By contrast, PIC-LM makes the provenance of outputs attributable to user input, allowing developers to place safeguards upstream. \n\nOur contribution follows work showing that safety alignment is shallow and brittle [11]. PIC-LM shifts the safety locus from the model’s parameters to auditable inputs. This can lead to more robust safety systems, such as pairing PIC-LM with a pre-hoc safety classifier (e.g., LlamaGuard) that screens for malicious claims. Therefore, while PIC-LM may not be the safest model in isolation, we see it as a promising building block for safety-aware systems where user-grounded control is critical. \n\nWe'll expand the limitations and broader impacts (App. A.4) in our revision. \n\nCitations\n\n[7] Maynez et al., 2020. On Faithfulness and Factuality in Abstractive Summarization\n\n[8] Huang et al., 2025. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions\n\n[9]  Geng et al., 2025. The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains\n\n[10] Chuang et al., 2025. SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models \n\n[11] Qi et al., 2024. Safety Alignment Should Be Made More Than Just a Few Tokens Deep"}]

Author rebuttal:

Meta-review: The paper proposes Precise Information Control (PIC), a framework for verifiable claim-level grounding in long-form generation. It introduces PIC-Bench, a benchmark covering eight generation tasks (summarization, biography, QA, etc.), and a training recipe that produces PIC-LM, an 8B model optimized for claim-level control. PIC requires outputs to include only the provided claims (full PIC: all claims; partial PIC: relevant subset). The authors show that even frontier LMs hallucinate in >70% of outputs under PIC, while PIC-LM improves full-PIC F1 from 69.1% to 89.7% and enhances factual accuracy in downstream QA and verification pipelines.

### Strengths of the paper:

- The reviewers (pcWR, UZLz, 8i1m) praise the novelty of claim-level control, noting that PIC reframes intrinsic hallucination into a precise, verifiable task that extends beyond vague binary factuality.

- PIC-Bench is comprehensive, spanning 8 diverse tasks and two evaluation settings, with rigorous claim extraction and verification protocols. This breadth provides strong potential for PIC-Bench to become a standard benchmark.

- PIC-LM achieves clear empirical gains, improving both faithfulness and precision, and showing meaningful benefits in RAG and chain-of-verification pipelines.

- The authors conducted extensive ablations and new experiments during rebuttal, showing robustness across prompt variations, scalability to models from 1B to 70B, and consistency across different LLM judges and human evaluation.

- The paper is well-written and carefully presented, with detailed analysis of remaining errors (distortion, fabrication, conflation), making the methodology transparent and reproducible.

### Weaknesses of the paper:

- Reviewers (UZLz, Dux5) note that PIC has conceptual overlap with RAG and attribution, and that its novelty could be more sharply distinguished. The rebuttal clarified differences (claim-level granularity, strict coverage), but some still see overlap.

- The framework depends on LLM-based claim extraction and verification, which may introduce biases or errors, though human evaluation shows strong agreement.

- Some reviewers (pcWR, 8i1m) expressed concerns about rigid constraints: penalizing even correct extra facts may reduce user value in flexible settings. The authors argue this rigidity is necessary for high-stakes domains but acknowledge tradeoffs.

- Evaluation beyond factuality is limited: while PIC-LM maintains fluency, more extensive assessment of linguistic quality and general capabilities would be beneficial.

- Ethical concerns were raised about PopB-CF, a stress-test benchmark where user-provided claims contradict world knowledge. While the authors justify its inclusion as a test of context compliance, some reviewers remain cautious.

### Primary reasons for Accept (Poster)

The paper makes a principled and timely contribution by formulating intrinsic hallucination as a claim-level control problem, operationalized through PIC-Bench and PIC-LM. This approach offers a stricter, verifiable, and reproducible grounding objective with demonstrated empirical benefits. While overlaps with RAG/attribution exist, the claim-level precision and enforcement of full coverage distinguish PIC as a useful new paradigm. Despite concerns about rigidity and reliance on LLM-based evaluation, the authors provided strong rebuttal evidence (human validation, judge-agnostic results, additional ablations), convincing reviewers that the work is technically solid, novel in framing, and practically impactful. Overall, it is a valuable addition to the NeurIPS program, meriting acceptance as a poster.

### Summary of the discussion and rebuttal

The authors provided detailed and constructive responses. For R-UZLz, they clarified PIC’s differences from RAG/attribution and added a RAG ablation without claim extraction, where PIC-LM still improved factuality. For R-pcWR, they reported new non-PIC benchmark results and ran human evaluations of coherence and fluency, showing PIC-LM outputs remain coherent and concise. For R-8i1m, they addressed terminology (faithful vs. factual), explained SFT performance drops, added error analyses, and reported results with and without PopB-CF to improve fairness. For R-Dux5, they demonstrated that PIC evaluation is robust across judges (GPT-4o, GPT-4.1, Claude 3.5) and validated against human annotation with strong agreement. Overall, the rebuttal substantially strengthened the paper’s empirical and conceptual contributions, and multiple reviewers raised their scores, converging on poster acceptance.
Decision: Accept (poster)