Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base
Authors: Linxin Song
Xuwei Ding
Jieyu Zhang
Taiwei Shi
Ryotaro Shimizu
Rahul Gupta
Yang Liu
Jian Kang
Jieyu Zhao
Venue: COLM 2025 (2025)
Keywords: Large Language Models
Evaluation
Misinformation
TL;DR: We propose stochastic error ascend, an optimization-based framework that efficiently identifies and refines failure modes in LLMs, discovering significantly more errors than existing methods while reducing evaluation costs.

Abstract: Large language models (LLMs) possess impressive linguistic capabilities but often fail to faithfully retain factual knowledge, leading to hallucinations and unreliable outputs. Understanding LLMs' knowledge deficiencies by exhaustively evaluating against full-scale knowledge bases is computationally prohibitive, especially for closed-weight models. We propose stochastic error ascent (SEA), a scalable and efficient framework for discovering knowledge deficiencies (errors) in closed-weight LLMs under a strict query budget. Rather than naively probing all knowledge candidates, SEA formulates error discovery as a stochastic optimization process: it iteratively retrieves new high-error candidates by leveraging the semantic similarity to previously observed failures. To further enhance search efficiency and coverage, SEA employs hierarchical retrieval across document and paragraph levels, and constructs a relation directed acyclic graph to model error propagation and identify systematic failure modes. Empirically, SEA uncovers 40.7× more knowledge errors than Automated Capability Discovery and 26.7% more than AutoBencher, while reducing the cost-per-error by 599× and 9×, respectively. Human evaluation confirms the high quality of generated questions, while ablation and convergence analyses validate the contribution of each component in SEA. Further analysis on the discovered errors reveals correlated failure patterns across LLM families and recurring deficits, highlighting the need for better data coverage and targeted fine-tuning in future LLM development.

Published as a conference paper at COLM 2025
Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base
Linxin Song", Xuwei Ding Jieyu Zhang?, Taiwei Shi", Ryotaro Shimizu',
Rahul Gupta', Yang Liuf, Jian Kang², Jieyu Zhao "University of Southern California, Uniiversity of Misconsin-MMddion
"University of Washington, ZZOO Research, Amazon, AGI, 'University of Rochester
O Code: github.com/uscnpplingg
Abstract
Large language models (LLMs) possess impressive linguistic capabilities but often fail to faithfully retain factual knowledge, leading to hallucina- tions and unreliable outputs. Understanding LLMs' knowledge deficiencies by exhaustively evaluating against full-scale knowledge bases is compu- tationally prohibitive, especially for closed-weight models. We propose stochastic error ascent (SEA), scalable and efficient framework for dis- covering knowledge deficiencies (errors) in closed -ossed-weight LLMs under a strict query budget. Rather than naively probing all knowledge candidates, SEA formulates error discovery as a stochastic optimization process: it iteratively retrieves new high-error candidates by leveraging the semantic similarity to previously observed failures. To further enhance search effi- ciency and coverage, SEA employs hierarchical retrieval across document and paragraph levels, and constructs relation directed acyclic graph to model error propagation and identify systematic failure modes. Empirically, SEA uncovers 40.7x more knowledge errors than Automated Capability Discovery and 26.7% more than AutoBencher, while reducing the cost-per- error by 599x and 9x, respectively. Human evaluation confirms the high quality generated questions, while ablation and convergence ana malyses validate the contribution of each component in SEA. Further analysis on the discovered errors reveals correlated failure patterns across LLM families and recurring deficits, highlighting the need for better data coverage and targeted fine-tuning in future LLM development.
1 Introduction
Large language models (LLMs) are pretrained vast corpora, including comprehensive knowledge sources such as Wikipedia. Despite this extensive exposure, LLMs often fail to retain or accurately reproduce factual information, resulting misinformation and hallucinations (Gekhman et al., 2023; Zhang et al., 2023; Manakul et al., 2023; Jiang et al., 2024; Yu et al., 2024). For instance, LLM mistakes France's capital Berlin (Simhi et al., 2024), or may fabricate plausible-looking citation by attributing a fictitious paper to real researcher in the relevant domain (Merken, 2025). These knowledge deficiencies pose significant challenges for downstream applications, particularly in high-stakes domains like healthcare, law, and scientific research, where factual accuracy is paramount.
To pinpoint knowledge deficiencies in LLMs, researchers typically construct static knowledge-intensive benchmarks for various domains (Chen et al., 2021; Hendrycks al., 2021b;a; Song et al., 2023; Su et al., 2024; Yue et al., 2024; Li et al., 2024b; Yang et al., 2024; Phan et al., 2025), and use them to reflect the level of LLM's knowledge. However, consider- ing the massive amount of knowledge that human society has accumulated, it is infeasible to curate static benchmarks that cover all of and test LLMs against it. Therefore, the need arises for versatile approach that can automatically uncover L.LM's knowledge deficiencies

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Published as a conference paper at COLM 2025
SEA: Stochastic Eroor Ascent
Anelyze Based on SEA Resulls
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Figure I: Overall workflow of stochastic error ascent (SEA). We search for closed-weight model's unknown knowledge iteratively from a given knowledge base until we reach the budget. The result from SEA can be further used to analyze the model's unknown categories and error patterns.
from a massive knowledge base, ideally discovering as many deficiencies as possible within limited evaluation budget for efficiency and scalability. Moreover, it should be applicable to closed-weight LLMs, given their widespread adoption for downstream tasks.
To this end, we propose stochastic error ascent (SEA, Fig. 1), scalable framework for uncovering knowledge deficiencies in LLMs under query budget. Exhaustively querying massive knowledge base is infeasible, so we iteratively select subsets that are likely to induce new model errors by formulating the task as a stochastic optimization problem. At each step, SEA approximates the per-step optimal subset by retrieving samples most similar to prior errors, leveraging the observation that model failures often exhibit shared characteristics (Li et al., 2024a). To enhance efficiency, we adopt hierarchical strategy finding the samples first at the document level and then at the paragraph level. We construct relation directed acyclic graph (relation DAG) that traces source-targe target error dependencies and prunes low- impact nodes based on cumulative errors, helping discover systematic weaknesses.
We conduct extensive quantitative and qualitative evaluations of SEA over eight commonly used LLMs. Compared to two baselines-Automatee Capability Discovery (ACD, Lu et al. (2025)) and AutoBencher (Li et al., 2024a)- SEA uncovers 40.7x more errors than ACD and 26.7% more than AutoBencher, while reducing cost per error by 599x and 9x, respectively. Human evaluation over 1,000 randomly sampled questions confirms 100% pass rate, i.e., all the model-gen -enerrtee questions in SEA are reliable. SEA exhibits consistent error discovery across steps, with all components contributing comparably, as shown in convergence and ablation studies. Correlation analysis reveals strong famill model alignmeett except for 01 -iimi Most models perform well on got-4o and gpt-4o-ming optimal subsets but struggle on DeepSeek-V3 ones. Error visualizations reveal two overlapping clusters, highlighting model-specific weaknesses and informing future data collection strategies.
Related work
Dynamic benchmarking. Static benchmarks (Chen al., 2021; Hendrycks et al., 2021b;a; Song et al., 2023; Liang et al., 2024; Yue et al., 2024; Li et al., 2024b; Yang et al., 2024; Wang et al., 2024b; Jain et al., 2024; Shi et al., 2024; 2025; Phan et al., 2025) suffer from data leakage and high building costs. Dynamic frameworks address this via automated, evolving data generation. Dynabench (Kiela al., 2021) integrates human-mooe model interaction, while Task Me Anything (Zhang et al., 2024a) enables scalable, user-driven evaluation. Recent work-DyVal (Zhu et al., 2024a;b), UniGen (Wu et al., 2024), and Benchmark Self- Evolving (Wang et al., 2024a)-empa refinement. AutoBencher (Li et al., 2024a) casts benchmark construction as optimization to surface model flaws but remains limited by static templates annotated data (Huang et al.,
probing agents and multi-agent systems for iterative
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Published as a conference paper at COLM 2025
2025). In contrast, SEA adopts fully adaptive, error-driven probing strategy using relation
DAG to uncover failures.
Model behavior understanding. We investigate factual knowledge modeling in LLMs, focusing on distinguishing correct from plausible-but-false outputs-a challenge rooted in their latent factual encoding (Petroni al., 2019). Techniques such gradient-guided prompt generation (Shin et al., 2020), systematic prompt design (Jiang et al., 2020), and tools like Automated Capability Discovery (Lu al., 2025) further expand the evaluation scope. EvalTree (Zeng et al., 2025) further reveals vulnerabilities via hierarchical capability trees. However, reliance on hand-crafted prompts or constrained queries limits their efficacy in surfacing nuanced misinformation.
Deficiency Discovery for Large Language Models
Consider we have a massive knowledge base K, closed-weight foundation model fclose and given budget C. K. includes N documents, each with an abstract and M; paragraphs p, i.e,e, K = ( L,.,,,;;; = 1,,,, M,}. Our goal is to find an optimal paragraph subset $ = PL. P
S) from K. that can maximize the error Ts(fclose) of the closed-weight language model fclose under the budget for felose (e.g,, the price for all tokens or a number of API calls). Specifically, we aim to solve the following optimization problem:
arg max Ts (fclose Sck
s.t. Σ ((S)
cost(fdose) C,
(1)
where
Ts(fooss) 18 (S) p/ ES
Σ
Σ (I.))Es(p!))
(2)
Eq. (2) denotes the average error of the weight model feloser where (x, is the multiple-choice question-answer pairs generated from each p)" by prompting question generator LLM g(.), and [| is an indicator function. To avoid the accidental error triggered by the prompt sensitivity of felose, we rephrase each question multiple times and generate semantically equivalent variants. To simplify, we use Tg for (s(cllose) in the later sections.
3.1 Stochastic Error Ascent
To achieve our optimization objective in Eq. (1), we propose a multi-step stochastic error ascent (SEA) algorithm. Generally, SEA iteratively performs error-based subset updates with hierarchical error-related retrieval until we reach the budget C. During the error-related retrieval, construct relation directed acyelic graph (relation DAG) with the source errors as nodes and error relations across each timestamp as edges. We further prune the source error by tracking the quality of the per-step source error. We present the overall algorithm in Alg. 1.
Baror-based subset update. Given large IC, it nearly impossible to let faose go through all questions generated from p E K. to find an optimal set paragraphs S that can achieve the (q,(1) under a limited budget. Therefore, we consider updating S iteratively. At timestamp f, we find batch of candidate paragraphs P: = ) Pp (P))) from K. that can maximize the probability of Tsuu,, > Ts,, which can be represented as:
S = S, U arg Pr (TS,PP, > Ts,)
(3)
Pyck
We solve (3) by focusing on paragraphs that resemble the error-inducing examples. Specifically, we target regions in K. that are likely to further challenge the model, resulting
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Algorithm 1: Stochastic Error Ascent
Input: Knowledge base K, closed-weight model fclase, budget C, random initial paragraph set B, threshold Č and 7.
Output: Optimal paragraph set $
p!t)
Initialization: f <- 1, cost 0, St Ø,
source f- Ø
while cost < C do
if 1 then
E
No source error at step 1; use random initial batch.
else
- F--nddiim(,) P,,,puu P!0 Source)
b (4): Sample an error related candidate batch. Update the target subset.
SH11 SUUE
plittle f- P!! Poource P T(p) > {
> Update source error set.
K. K < (P))
b Remove new source error from IC to avoid loop.
91+1 + (Psouner & (P (oourree Psounce)
10
>> Update relation DAG.
p(tt1) f pllttl {pE plltt
TG (p) <7)
E5):)): Pruning sourre b Update cost. We did one-time inference on E.
cost + (Fst(TT((e (fclss))
12
13
++1
return S
14
in retrieving an error-related batch E that is semantically similar to set of source para- graphs: (t) C U 1 Sj. Here, (() comprises paragraphs for which the LLM exhibits high error rate (i.e., [[p] > č for all Pource with being predefined error threshold). Practically, retrieve the error-related batch E by ranking candidates from K. based their semantic similarity to Piluueer using tools such as Sentence Transformers (Reimers Gurevych, 2019), and selecting the top-k candidates. Given Sentence Transformer fs : Rd, where d is the embedding dimension, we can define the error-related batch as:
) (11((PA)11(((000)
E = --FFiddiimXX. (K, p(t) Psource)
U Topx (
(((Ps)) f6(p)) f((C)
Pe
P
(4)
PaCp!ii
where = (f=(P)). (f
To improve the efficiency of calculating (4. we perform hierarchical retrieval from document to paragraph levels. We first pre-process the embeddings for the abstract of all documents d, E K. as Dabs where d, contains
(f(
of can- didate error-related documents D. by performing (4) between Dabs and Psource i.e., D. FindSim(Pate. P P. Finally, we retrieve error-related batch E by comparing the paragraphs in document D. and source errors, i.e., E = F FindSim(D, ( In this way, we finish the hierarchical retrieval.
set of paragraphs that can be represented as { We then retrieve
Relation DAG construction and source pruning. To identify systematic issues in falsse such as flaws localized within specific regions of the knowledge base, we construct relation DAG (P(f)) ( P(tt))) ) G is constructed by linking each paragraph p E P!t) to its top semantically similar error-inducing paragraphs in p((+1) based on the hierarchical error-related retrieval described above, forming directed edges that represent potential error propagation paths. We then assess the quality of source based on cumulative error. We define the cumulative error ngg (p) for paragraph E Psource as the average error across its descendants' error:
mc(p)
Σ
T(p)
55)
|Decc (p)] vcDesce(p)
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Published as a conference paper at COLM 2025
where (p) denotes all descendants of p that can be reached via the edge space &. We prevent loops in & by removing new source errors in each step from K. (line 11 in Alg. 1). We then perform a threshold filter according to (p(pp) at each step to prune Psource.
Comparing Stochastic Error Ascent with Baselines
Knowledge base details. We collect large-scale English-based knowledge base from Wikipedia (Wikipedia contributors, 2004), comprising 7.1M documents and 28.8M para- graphs across 13 top-level categories with hierarchical subcategories. Each Wikipedia page is a document with a preprocessed abstract, and we map sections in the document as para- graphs. To enable efficient retrieval, we pre-embed each page's 's title and abstract using Sentence Transformer and further process the embeddings via FAISS (Douze et al., 2024) with vector quantization and an inverted file system. Additionally, we convert HTML content to markdown to support on-the-fly paragraph-level retrieval.
Evaluation protocol. We use an LLM generator (go to generate and rephrase questions for each paragraph. Specifically, the LLM generator will take a paragraph as input and output a list of questions in JSON format. We perform the human evaluation of the generated questions and put the quality analysis in Sec. 5. We compute accuracy for the questions answered by all models by comparing the ground truth answer provided by the LLM generator and the answer from the testee models. We formulate all the questions with the same template for all testee models.
Implementation details. For felose selection, besides widely used closed- models, we also simulate open-weight models in the closed-weight setting to expand the scope of evaluation. Specifically, we evaluate GPT-series models: gpt-4o (OpenAI et al., 2024), gpt-4o-mini (OpenAI, 2024a), and ol-mini (OpenAI, 2024b), and DeepSeek-serise mod- els: Deepseek-V3 (Liu al., 2024), Deepseek-R1, and R1 RI-Distill-LLama--00 (DeepSeek- AI et al., 2025). We also include two widely used models hosted on DeepInfra': Qwen2. Instruct (Qwen et al., 2025) and Llama-3. 3.3-77B-Instree (Grattafiori et al., 2024). We adopt mGTE (Zhang et al., 2024c) as our Sentence Transformer model for hi- erarchical retrieval. We set the decoding parameters to temperature 0.1 and top-p 0.9 for deterministic responses. Thresholds for source error judgment and source error pruning are set to Č = 0.5. We use the average accuracy across the 25 generated questions for source error judgment. We set the as 50 in (4) and randomly select 40 paragraphs from the retrieved paragraphs as the error-related batch E. For the initial batch B, we uniformly retrieve 40 paragraphs from 13 predefined Wikipedia categories.
Baselines and budget settings. Research on automatically generating questions to self- discover misinformation and vulnerabilities in LLMs remains limited. We select two closely related methods, Automated Capability Discovery (ACD, Lu et al. (2025)) and Auto- Bencher (Li et al., 2024a), as baselines to compare the ability of error discovery and cost. ACD is an automated method that leverages LLM's internal knowledge to self-discover some surprising successes and failures, while AutoBencher takes an input topic and retrieves related pages iteratively from given knowledge base to build challenging benchmark. Both methods are motivated by the need to overcome the high demand for human labor and the' limited ability of traditional evaluations, aiming to automatically explore models' capabilities and shortcomings. In our experiments, we use gpt-4o as the generator model for task/ / benchmark generation for ACD and AutoBencher. We let ACD take one of the Wikipedia topics as seed tasks for task generation, and let AutoBencher use all categories as interest topics and generate 13 benchmarks, including 2,000 questions in total. We adopt two different budget settings for SEA according to the features of ACD and AutoBencher. Specifically, we set the budget as 20,000 API calls for felose plus the cost for QA generation when comparing with ACD, and the budget as AutoBencher to generate 13 benchmarks when comparing with AutoBencher.
hntpps://deepinfra.com//
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Errors by ACD SEA over Wikipedia Categories
Cost per Error
ACD
306
SEA
# e77075
30-
302
Error Rate of AutoBencher and SEA among the Same Problem Size
Coxt
Empor Rate
0.4
0..44
333
6/e
3.5
0.29
0.1
Auisfennhher
010 3.866
SEA
DepSelk-R
Davtil: Zlana 708
DeepSesl-Rt
DeepBeok-1 Vg
Llans Llama-133220 2-200 Instract
Qreen S-72k S-22k Insiruel
ipt-4o
apt toi-io-mini
ol-mini
AuttoBencher SEA
Figure 2: Comparison of errors discovered by ACD, AutoBencher, and SEA. We compare ACD with SEA among the same budget while comparing AutoBencher among the same question size. For ACD, we summarized the number of failed tasks, and for SEA, we summarized the number of source errors. We let AutoBencher create 13 benchmarks, each of which takes of the Wikipedia categories interesting topic. We let SEA search the same number questions according to each model. ol-mini failed on ACD due to the violation of the prompt usage policy from OpenAl.
4.1 Comparing with ACD
We first compare the number of errors ACD and SEA discovered among the same budget, including question generation and testee inference. Specifically, let ACD start searching from free-style handwritten task conditioning on one of the 13 general Wikipedia categories and summarize the error tasks discovered by ACD. Error tasks and source errors both reflect LLM misinformation in category. Therefore, we summarize the comparison results between error tasks and source errors found by ACD and SEA, respectively, for each model across 13 categories in Fig. 2. We can observe significant gap between the number of errors found by SEA and ACD. SEA can discover most 55.83 times as many as the errors of ACD on DeepSeek-Vy -3 model. The main reason is that ACD does not rely on an external knowledge base but relies only on LLMs' internal knowledge, which requires more budget to guide LLMs into deeper regions of the error space.
4.2 Comparing with AutoBencher
We compare the error rates questions generated by SEA and AutoBencher. Specifically, let AutoBencher generate 13 benchmarks, each corresponding to distinct Wikipedia category as the input topic. We then concatenate all benchmarks as one (resulting in 2,000 questions) and evaluate all models based on it. We compare the error rate between the Auto- Bencher benchmark and the етrог rate on an equal number of questions generated by SEA.
The results are summarized in Fig. As shown in Fig. 2, SEA outperforms AutoBencher across all evaluated models in terms of error rate. Specifically, on DeepSeek-Va. SEA achieves an error rate of 0.42, substantially higher than AutoBencher's 's 0.26, indicating a 61.5% rel- ative improvement in error detection capability. Similarly, for SEA records error rates of 0.42 and 0.39, surpassing AutoBencher by margins of 13% and 30%, respectively. The average error rate across all models is 0.38 for SEA versus 0.30 for AutoBencher, reflecting a 26.7% increase in error detection efficiency.
and
Qwen2-72B-Instruee
Analyzing Stochastic Error Ascent
Validating with human evaluation. this study, we utilize gpt-4o as the question-answer generator. We assess the quality of its outputs through human evaluation involving

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Cumulative Ts(fews)
Perstep Error. Telfeinse)
DeepSeck-RR
2wwn22.572B-Instrrun
0.40
0.30
8pt-éo-mini
0.40
gpt-4o
-lama-3.3-70-Instrrre
ol-mini
0.20
0.20
DeepSeek-V3
Distill-Llamm 70B
Step
Step
Figure 3: Per-step error TE (fclse) and cumulative error Ts (fsse) ) for each model. We observe that the errors of all models are positively related to step, indicating SEA gradually and continually find the model's knowledge deficiencies from the knowledge base.
Qwen25-728-Jnsiruc
spI-40
1.ama-33.-708-nnste
DeepSeck-V3
Cunulative Error
0.43
0.6
0.45
0.35
w/j Pruning
0.35
0.30
0.30
0.35
Random
0.30
0.0
025
0.55
023
0.30
020
0.00
033
12
20
Step
Step
Step
Figure 4: Ablation studies on the component contribution of SEA. We compare SEA with its two variants: without source pruning (i.e., pass the lines 10 and 11 in Alg. 1) and random selection (i.e., pass the lines 9, 10, and 11 in Alg. 1). We observe that each component contributes equally to SEA.
five college-level students, who verify the truthfulness of the generated answers by cross- referencing them with corresponding paragraphs in the knowledge base, mirroring the input provided to the generator. From 1,000 randomly selected questions generated across 20 steps, our evaluation achieved 100% human pass rate, confirming that all answers were both present and correct within the associated paragraphs, consistent with the results in Li et al. (2024a), which also uses LLM as question generator with Wikipedia documents.
Convergence analysis. To analyze the convergence behavior of SEA, we conduct an empiri- cal study, given the inaccessibility internal activations in wleshd-weight models during the search process. We track both the cumulative error T's (fclose and per-step error Te(fclose over 20 iterations SEA (Fig. 3). For cumulative error (left), we observe steep initial increase followed by plateau across all models, indicating that SEA rapidly identifies high-impact errors early in the search, then gradually uncovers subtler or less frequent failure modes. Notably, models such 01 -i-mmi and R1 Ri-Distill-Llame peak cumulative errors, while DeepSeek-a R1 shows a more gradual ascent. Per-step error (right) further highlights the adaptability of SEA, as consistently uncovers challenging inputs. The slope of per-step error varies across models: gpt-4o-mini shows a relatively flat trajectory, while m1min and DeepSeek-V3 show steeper climbs.
exhibit higher
Ablation studies. SEA has two significant procedures: (1) collecting and updating the source error set (line 9 in Alg. and (2) directed graph construction and source pruning (lines 10 and 11 in Alg. 1). Procedure Two relies on the result from Procedure One. To analyze the contribution to both procedures, we conduct two ablation experiments on four selected LLMs (Qwen2.5 5-72B 22BB-Insruct. gpt-4o, Llama-3, 1.mm-33.--0BB 3-78B- -e00--Instrctt. and DeepSeek-V3) by removing procedure one and both procedures to create two variants of SEA: (i) w/c pruning and (ii) random selection. For the random selection, we change all error-related batches into random batches where we randomly select paragraphs from the knowledge base. We summarize the results in Fig. 4, from which we observe that the contribution of each procedure to SEA is linearly increasing. The cumulative error of random selection barely increases, while the gap between SEA and / pruning variant starts increasing after
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Model Cost
DeepSeokeRt
R1-Distil--lamaa
of-mini
DeepSeek-V3
T Tlama-33-708
Qwen22.-77B
gpt-fo-mini
gpt-6o
Generation Cost (US 5)
28, 163
28,600
31.094
30,206
29.776
29.562
28.243
32,807
Inference Cost (US 5)
48 360
39
1.261
0.347
7,906
Inference Outpui Tokens
19,608,736
10,8368822
8,507,015
1,004,942
125,177
308,445
Table Question generation cost, inference cost, and output tokens at inference time across 20 steps (results in 3;g.3; 20,000 questions in total). We can see a significant gap between reasoning models (DeepSeek-Ree R1, R1 RR--iittlll sestillllllee -Llama 708, and al ol-mini) and other non-reasoning models.
Result Corelaatonn Between Testee and Provider
Testee Accuracy on Provider's Test Set
13:9-333-08
029
Dietil-Llmma-908
DeegSeel-81
0:688
0.463
6.520
00
0.52
0.537
0.526
0.5
DespSeck-V3
0.419
6.695
35
0.542
0.569
0.996
Tedee
Qwen255-28
-Instruct
0:649
161
0.451
1.000
0.841
0 840
097
0:515
0.5%6
0
0.537
0.511
0.624
055
030
al-mini
0,400
0.683
0.41.
0.824
2,000
0.472
0.66
0.30
0.394
0.351
0.422
04.8
0.005
0.515
gpt to miled
0.495
0.505
0.437
0.472
6.522
LOCO
0.17
0.451
6.699
0.461
0.302
C5L8
600
0.586
647
spt-6a
0.428
8 462
670
0.769
0.648
0 A25
103
84
0.621
0.562
0/4
0111
0714
0
Llana-3--700 3-7r0 -eotesse
Dusll Zlana 7088
Demiiekk
DerpBiek 1/3
Qoeen2.5775 Ievrree
mi-man
api 4o moni
8pt-4o
Linena 1lmns-3.---0 3.2-7B
-estrett
Deepsina--h Disiil Lama PRIB
DespSiene- 4/7
QweaddS-77m Jpveracd
of yaund
est mini
Suboet Provider
Subsel Prosider
Figure 5: Comparison of cross-validation between each model. X-axis indicates the subset provider (i.e., S provider; sourced from experiments in Fig. 3), and Y-axis denotes the testee. We summarize two results: (1) correlation between testee result and provider result, and (2) accuracy of testee on each provider's 's results. The higher the correlation, the more similar the answers of the two models are. Similarly, the higher the testee's accuracy, the more challenging the provider's 's question.
a few steps, indicating that low-quality sources that haven't 't been pruned by the cumulative error start negatively affecting SEA..
Cost Analysis We summarize all the model's costs for Fig. results in Tab. We use cloud API served by DeepSeek and DeepInfra for all open-sourced models (DeepSeek-R1, RI-Disti11-LLamm--eee DeepSeek-V3, Llama-3. 3-708, and Qwen2. Qw2B), and our cost calcu- lation is based on their token price per million tokens. We observe that the variance for the generation cost is low, while that for the inference cost is high. A significant difference can be discovered by looking into the inference cost between reasoning models (DeepSeek-R1, R1-Distill--lama-PP0
and 01 o1-mini) and other non-reasoning models. DeepSeek-R1 has extremely long inference token length even for the multiple choice questions, which causes the highest cost, though its price-per-token is lower than 01 -iini and gpt-4o.
6 Analyzing LLMs from the Discovered Deficiencies
Query 1: How does the model perform on other LLMs' optimal subset? In this study, we search for eight LLMs' deficiencies and consequently create eight unique optimal subsets according to Eq. 1. We conduct the cross-validation by testing each model (testee) on the other models' (providers) subsets, with correlation and accuracy results summarized in
Fig. 52. Our analysis revealed an asymmetrical correlation pattern, where the direction of testing significantly influenced outcomes for instance, gpt-4o-ming as the testee ex- hibited a high correlation (0.917) with gpt-4o as the provider, yet the reverse yielded notably lower correlation (0.423), suggesting that gpt-4o-mini may be distilled variant optimized to emulate gpt-4o's behavior, while its subset highlights divergent behaviors. Models from similar families, such as got-4o and got-4o-mini, demonstrated higher cor- relations, indicative of shared output patterns, whereas cross-family comparisons, like
We omit the DeepSeek-R1 due the budget limitation.
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Sook
Elana d 5-58 fnstrrt, Ri-Deaill- Ulama-00n, ii, and Deepderk-V.. Mannty about bitory eventt,
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also includes culture the and general relemmzer
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general adiansaa sasily and astel adtntoo
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apite, Deep
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mon Toptc (Shup))
Tachnology appllaplladdee
Goonn reference
Histery and evet
Goograaty piaows
Instrct Matnly
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dbou ledwndagy and applad retrrnees rahma
Homnnnattion activittss
oloming: Musntly about cuiture and neianona, philoophy and Hinsking
ploskaalctanos
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Frends
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Figure 6: Error distribution of each testee model. We search with the same initial set from thirteen categories of Wikipedia. We visualize the results by t-SNE without clustering algorithm. Each point in the figure denotes the corresponding model's s source error p E Poourcee Different colors denote different models, and different markers denote different categories. We can observe natural clusters of each model discovered by SEA according to their knowledge omission areas.
Cluster ID
Models
Main Categories
Error Pattern
(1) Challenging Chronological Analysis
4o
(2) Unfamiliar with Locational Details
3
DeepSeek-V3 ol-wini
Culture and the arts
(3) Issues in Pattern Recognition
(4) Inaccurate Data Synthesis
(5) Collaberative
Relational
(1) Challenges with Chronological and Historical Data
Owen2. 5-726-Innttroe Instcctt
(2) Issues with Contextual and Performance-Rllaed Information
Health and fitnees Natural and physies science
Llena 3.3 3.3-788- 788 Instruct
(3) Inaccurate Interpretation of Pattems and Trends
R1-Distill-Llane-66
(4) Over-reliance on Assumptions and Generalizations
(5) Difficulty with Contextual Associations and Identifications
Table Error patterns for models in cluster 3 and 5 in Fig. We aggregate error patterns from the question level to the paragraph level and finally to the model level.
3. versus gpt-4o, showed moderate correlations (0.4-0.5). The accuracy matrix illustrates model performance across subsets generated by different providers, with go and gpt-4o-mini's 's subsets being less challenging, yielding higher accuracies from testee models, while DeepSeek-V3's subset posed a greater difficulty, and ol-mini consistently underperformed, underscoring varying model capabilities and subset complexities.
Query 2: What kind of knowledge do the models lack? To investigate this, we visualize
the source error p E Psouree in of each model by compressing the embedding of P Psounce with t-SNE (Van der Maaten & Hinton, 2008) in Fig. 6. We mark different models in different colors, and different Wikipedia categories in different marker shapes. We can see that gpt-4o, DeepSeek-V3, and o1-mini overlap highly, with errors concentrated in culture and the arts. gpt-4o-mini and DeepSeek-NP have unique clusters, less overlapping with other models, while both weaknesses include history and events, and society and social science. We also no- tice that DeepSeek-V3, 11-Distill-L1ama-7000
and Qwen2.5 5-728-Instruct have error over- lap on health and fitness, and natural and physical sciences, while also has its only cluster on technology and applied sciences, general reference, natural and physical sciences, and history and events. Errors from Llama-3. 3. I-7tB-Instruct have over- on history and events, indicating the possible inherent relationship between these two models. We observe that a wide range (5 out of 13) of categories can trigger 01 -mini's error, including culture and the arts, society and social sciences, general reference, philosophy and thinking, and history and events, indicating a significant gap of training data and training strategies between 01 -1-mini and other models.
lap with DeppSek-Ri--iisiii----mmmm

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Query 3: What pattern causes model to fail in a specific category? To understand the behavior of a model's enror, we aggregate the question-level error into paragraph-level errors and further summarize them as the model's enror behavior. We choose clusters 3 and 5 in Fig. 6 and perform the multi-level aggregation by prompting an LLM to retrieve question-level error pattern, paragraph-leva pattern, and finally the model and cluster-level error pattern. We only analyze clusters 3 and 5 for budget reasons and summarize the error patterns in Tab. 2. The models in cluster appear to have difficulties with tasks requiring historical context, spatial awareness, and relational reasoning in the domain of culture and arts. The models in cluster seem to have broader issues with contextual understanding and precision, particularly in domains requiring empirical rigor. Both clusters exhibit challenges with chronological analysis and pattern recognition, indicating that these might be common limitations across various LLMs when dealing with complex domains.
Conclusion
In this work, we introduced stochastic error ascent (SEA) that can discover knowledge deficiencies of language models on a massive knowledge base. SEA identify knowledge deficiencies closed closed-weight LLMs by framing it as a budget-constrained stochastic opti- mization process. SEA surpass previous baselines, including ACD and AutoBencher, by uncovering 40.72 times and 26.7% more errors, respectively, at 599 and times lower cost per error. SEA achieves 100% human pass rate on generated questions, exposes distinct error clusters across models such as gpt-4o, neepSeek-V3, and 01 oi-mini, and delivers critical insights for enhancing model reliability.
8 Future works
Generalizing to other modalities. In this work, we discuss the possibility of searching massive knowledge base by SEA. However, we
an LLM's knowledge deficiencies did not extend it to the multimodal domain, such as images and videos. The difficulty of high-quality question-answer pair synthesis from the multimodal domain limits the extension because SEA requires high-quality question as the base for deficiency searching. Zhang al. (2024a) suggests a possible solution for generating a benchmark from massive image base, including 2D and 3D scenes, but they leverage human-level annotation for each image. Zhang al. (2024b) further adopts low-level task-specific models for automatic image annotation, while the quality of the generated annotation is low. Such low low-quality annotation further affects the quality of the question. Given the low quality of questions synthesized from images, it is hard for SEA to generalize to the image domain. Future work may focus on generating high-quality annotations from an image, enabling noise-reduced evaluation by SEA.
Enlarge the searching scope. SEA can search LLMs' vulnerabilities across massive knowledge base, but this result is affected by the initial set. Although we demonstrated that SEA works well on different initial sets (as shown in Fig. 6 and Fig. 7) under conditioning and fully random settings, its searching scope is still limited by the high cost of close-weight LLMs. We discuss a possible solution for extending the searching scope by fitting a small model to the model's existing error in Appendix A, but it cannot fit the model's failure pattern well, resulting in less than 70% of accuracy the test set. Future work may focus on how to enlarge the scope of the search by effectively capturing the failure pattern from the input model.
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DespSeek-13
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Figure 7: Error distribution of each testee model. We search with the same random initial set from Wikipedia without specifying specific topics. We visualize the results by t-SNE without clustering algorithm. Each point in the figure denotes the corresponding model's source error p E PPource Different colors denote different models, and different markers denote different categories.
A Extra Analysis and Case Studies
Query 4: Will LLM produce misinformation on its unknown knowledge? In order to investigate this question, we randomly sampled 5 questions from the gpt-4o optimal subset, specifically selecting those where SEA previously identified factual errors or vulnerabilities in LLMs, representing unknown knowledge. In this experiment, we modify these questions into free-response format using got-4o and retest them got-4o. Errors such Tab. 3 (incorrect doctoral year), Tab. 4 (misidentified event), Tab. 5 (wrong attribution of artist and medium), Tab. 6 (misattributed venue), and Tab. (erroneous exhibition location) clearly show that when LLM encounter unknown knowledge, they may produce misinformation, as highlighted by the underlined errors each example.
Query 5: Will LLM exhibit memory-context conflict when answering questions related to the detected deficiencies? Memory-context conflicts are known as conflicts between pre-trained parametric knowledge and retrieved information (Su et al., 2024; Zhao et al., 2025). To evaluate whether LLM exhibits such a memory-context conflict when addressing questions probing their deficiencies, we randomly sampled 1000 incorrect questions from the gpt-4o QA set and augmented each with its corresponding retrieved factual context, testing on go.-4o. Despite this external supplementation, the accuracy of gpt-4o improved only to 28.6%, indicating that the model adopts the provided context in merely about one-third of cases while predominantly relying on pre-trained internal knowledge in the remaining instances. This outcome indicates that even essential external-augmentee not sufficiently override L.MM's s entrenched memory. Therefore, our findings mean that LLM do exhibit clear memory-context conflict. The relevant testee prompt is listed in Tab. 12.
information may
Query 6: What deficiencies can be discovered from a random initial set without cate- gory constraints? As described in Sec. our random initial batch is uniformly sampled across 13 Wikipedia categories. However, the downstream task may lack well-defined categories. To investigate the solution, we test six models (gpt-4o, mpt-4o-mini, 01 ol-mini, Qwen2. S-72B-Innstruct DeepSeek- -1 a complete random initial batch without any topic constraint when performing SEA. We search each model for 20 steps with the same setting described in Sec. 4, sum- marizing the topic from each model by LDA (Blei et al., 2003) and aggregating the topic from all models into 10 general topics, including: Baseball, American Football, Projects, Swimming/Pen http///aallmpccollymmo/a/o Sports, Music, Soccer/Associattion Football, Simple Time/Date/Daa Mentions, College Football
and DeepSeek-V3) by adopting
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Example
Title:
James B. Stump/Career
Original question:
What year did James B. Stump receive his doctoral degree from Boston University?
Ground true answer:
2000
Modified question:
Discuss the year in which James Stump was awarded his doctoral degree from Boston University.
Misinformatton:
James B. Stump was awarded his doctoral degree from Boston University in 1998.
Table 3: Example for query 4. The correct doctoral year is "2000", but the misinformation incorrectly states "1998", The incorrect information has been highlighted using underlines.
/eeassse//esss,, and Russian Localities/Distriee These topics are mainly sports and health, identifying systematic failure patterns of dif- ferent LLMs in this area. We further visualize the result of the source error in Fig. 7. We first observe similar distribution as in Fig. 6, where gpt-4o, DeepSeek-V3, and 0f o1-mini share similar failure patterns, while Qwen2. 5-728-Inntrruc and DeepSeek-R---Dittii--mLmmmm-- share similar failure patterns. We notice large volume of DeppSeek-i--isttil--Lamaam and 0l -1-in aligns with the observation Fig. We also observe that gpt-4o, DeepSeek-V3, and ol -mini mainly fail in music-related paragraphs, while gpt-4o-mini mainly fails in Baseball and Transportation project-related topics.
Polls/All-Anerinn Lists,
extend the searching scope? Following the settings in Zhang al. (2024a), we try fitting a BERT (Devlin et al., 2019) model to identify if a paragraph from knowledge base trigger LLM's error. We first collect 4,402 retrieved parágraphs from 50 rounds of SEA searching process on gpt-4o. We annotate the paragraphs as 0 if the aver- age accuracy across the generated questions is lower than 0.5, and 1 otherwise. The collected paragraphs are split into training, validation, and test sets, respectively, with the ratio 8:1:1. We adopt early-stopping to prevent overfitting according to the validation respectively. The performance. We baase-uncssed
Query 7: How
tried the bert-base-un uncaaed and bert-large-uncal achieves 66.22% average accuracy on the test set, while bert-base-uncased achieves 67. 67.85% average accuracy. These results suggest that larger BERT models can capture the subtle semantic cues that differentiate paragraphs likely to mislead an LLM. However, the overall performance indicates that this is challenging classification task, potentially due to the noisy or indirect relationship between paragraph content and downstream model behavior.
B Prompt of SEA
This section is supplemented with some additional details when implementing the pipeline of SEA, which is introduced in Section 3.1. To be more specific, Tab Tab 9, Tab 10, Tab 11, and Tab 12 are prompts for multiple choice question generation, question rephrasing, analyzing error pattern, SEA testee model, and testee model in query respectively.
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Example 2
Title:
Mimi Cherono Ng' Dg'ok/Grouup exhibitions
Original question: In which event was the series "Do You Miss Me? Sometimes, Not Always" by Mimi Cherono Ng' ok included?
Ground true answer:
African Photography Encounters
Modified question:
Discuss the event in which Mimi Cherono Ng'ok's series "Do You Miss Me? Sometimes, Not Always" was included. Provide details about the event and its significance.
Misinformation:
Mimi Cherono Ng'ok's photographic series "Do You Miss Me? Sometimes, Not Always" was included in the 2019 Whitney Biennial, a prestigious contemporary art exhibition held at the Whitney Museum of American Art in New York City. The Whitney Biennial one of the most significant events in the art world, known for showcasing the latest trends and emerging voices in American art. It serves barometer of contemporary art in the United States, often highlighting diverse per- spectives and innovative practices. The 2019 edition of the Biennial was particularly noted for its focus on issues of identity, social justice, and the political climate, re- flecting the broader cultural and social dynamics of the time. Mimi Cherono Ng'ok's work, which often explores themes of memory, displacement, and emotional land- scapes, fit well within this context. Her series "Do You Miss Me? Sometimes, Not Always" delves into personal and collective narratives, capturing the nuances of absence and longing through evocative imagery. The inclusion of Ng' dg'ok's work in the Whitney Biennial not only elevated her profile an artist but also contributed to the broader discourse on contemporary African art and its place within the global art scene. Her participation underscored the Biennial's 's commitment to presenting diverse artistic voices and addressing complex themes that resonate with audiences worldwide.
Table 4: Example for query 4. The proper event is "African Photography Encounters, yet the misinformation erroneously identifies it as the "2019 Whitney Biennial". The incorrect information has been highlighted using underlines.
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Example 3
Title:
Museo Nacional Centro de Arte Reina Sofía/ Sofia/Gallis /aallery
Original question:
Identify the artist and medium for the 1923 painting 'Pottrait of Tristan Tzara'.
Ground true answer:
Robert Delaunay, oil on cardboard
Modified question:
Describe the artist and the medium used for the painting 'Pottrait of Tristan Tzara' created in 1923.
Misinformation:
The painting 'Portrait of Tristan Tzara' was created by the Romanian avant-garde artist Marcel Janco in 1923. Marcel Janco was key figure in the Dada movement, which was known for its anti-establishment and anti-art sentiments. The medium used for this painting is oil on canvas. Janco's work often incorporated elements of abstraction and cubism, reflecting the experimental and revolutionary spirit of the Dada movement. Tristan Tzara, the subject of the portrait, was prominent Dada poet and one of the movement's ss founders, making this painting significant piece in the context of early 20th-century avant-garde art.
Table 5: Example 3 for query 4. It shows that the true artist and medium are "Robert Delaunay, oil on cardboard", while the misinformation wrongly lists "Marcel Janco" and "oil canvas". The incorrect information been highlighted using underlines.
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Example
Title:
Paa Joe/Exhibitions/See Solo and group exhibitions
Original question:
Identify the venue that hosted the "Six Feet Under" exhibition during the years 2007 and 2008.
Ground true answer:
Deutsches Hygiene-Museum, Dresden
Modified question:
Describe the venue that hosted the "Six Feet Under" exhibition during the years 2007 and 2008
Misinformation:
The "Six Feet Under" exhibition was hosted at the Kunstmuseum Bern in Switzerland during the years 2007 and 2008. The Kunstmuseum Bern renowned for its extensive collection of fine art, ranging from the Middle Ages to contemporary works. The museum is situated in the Swiss capital, Bern, and is known for its modern architectural design that complements its historical setting. The "Six Feet Under" exhibition explored themes related to death and the afterlife, featuring diverse array artworks that engaged with these profound topics.
Table Example for query 4. Example 4, the accurate venue is "Deutsches Hygiene Museum, Dresden", but the misinformation mistakenly mentions "Kunstmuseum Bern". The incorrect informa- tion has been highlighted using underlines.
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Example 5
Title:
Clare Kenny/Selecterr exhibitions
Original question:
Which art space featured Clare Kenny's "If I was Rich Girl" in 2019?
Ground true answer: Kunst Raum Riehen
Modified question:
Identify the art space that showcased Clare Kenny's exhibition titled "If I was Rich Girl" in the year 2019, Provide details about the venue and its significance in the art community possible.
Misinformation:
Clare Kenny's exhibition titled "If I was a Rich Girl" was showcased VITRINE in 2019. VITRINE is a contemporary art gallery located in London, known for its unique exhibition format that utilizes both a window gallery space and interior gallery. This allows for art to be viewed from the street, making it accessible to a broader audience. VITRINE has gained a reputation for supporting emerging and mid-career artists, providing them with a platform to present innovative and experimental works. The gallery's commitment to showcasing diverse and thought-provoking has made it significant player in the contemporary art scene.
Table 7: Example 5 for query Original testing process correctly names venue as "Kunst Raum Riehen", in contrast to the misinformation's 's incorrect attribution to "VITRINE" The incorrect information has been highlighted using underlines.
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Prompt for Multiple Choice Question-answer Pair Generation
According to the following title and context, reply with (num_offgpp _f_ multiple-choice questions and a statement based on the question.
## Title [title]
## Context (Only you can see this context) (context)
## Requirement
Each question should include options.
The context is only for you to generate the question. Therefore, your question should redirect to this context, like "according to the provided context..."
Your question should include enough information that can help the testee to understand the background.
Your reply should follow the given JSON format.
## Reply format
"question": "QUESTION CONTENT",
"options": ["A: "B: ... "C: "D: ..."]. "],
"statement": "STATEMENT OF THIS QUESTION." " "answer": "CHOICE FROM THE OPTIONS. For example, A" },
]
Table The prompt for multiple choice question generation.
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Prompt for Question Rephrasing
According to the following title, context, and question, reply with Inum_of, qa) rephrased questions and the corresponding answers. Your question should provide sufficient content avoid ambiguity. You should reply JSON format as follows:
## Title [title]
## Context (Only you can see this context)
(context)
## Question
lquestion
## Requirement
Your question should have the same meaning as the provided question, only rephrased.
The context is only for you to generate the question. Therefore, your question should not redirect to this context, like "according to the provided context..."
Your question should include enough information that can help the testee to understand the background.
Your reply should follow the given JSON format.
## Reply format
"question": "QUESTION CONTENT",
"options": ["A: ...", "B: ... "C: "D: "J,
"statement": "STATEMENT THIS QUESTION."
"answer": "CHOICE FROM THE OPTIONS. For example, A" }
]
Table The prompt for Question Rephrasing.
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Prompt for Analyzing Error Pattern
Given a context, a question, and its corresponding incorrect solution, generate gerund phrase that thoroughly and precisely describes the **specifi*** skill or capability lacking that causes the enror.
## Context (context)
## Question lquestion)
## Correct Solution (answer)
## Incorrect Solution
[llm answer)
## Requirement
The incorrect Solution is provided by testee who cannot access the context. Your answer should not mention that the skill is related to context information retrieval. The skill description should be an action-oriented gerund phrase that is **informa- tive** and **detailed**.
The phrase should refer to ""specifi** skill capability that comprehensively covers the key aspects of the solution, without including any context or specifics
from the question or solution.
Avoid unnecessary elements unrelated to the core capability.
Please output "*only gerund phrase"* describing the skill, with NO additional
text.
Table 10: The prompt for Analyzing Error Pattern.
Prompt for Testee Model
Given the topic: (topic), answer the following question by choosing one option in Options:
Question:
(que]
Options:
(opts)
Your Answer (put your answer in Nbox()):
Table 11: The prompt for Testee Model.
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Prompt for Testee Model in Query
Given the topic: [topic), answer the following question by choosing one option from Options below:
Question:
!question
Retrieved Fact:
[input]
Options:
[options]
Answer:
Table 12: The prompt for Testee Model in Query
27


Reviews:
[{"review_id": "gp4m2nnLqL", "reviewer": "Reviewer_cTGP", "summary": "This paper focuses on knowledge deficiencies discovery in language models within a strict query budget and massive knowledge base. The proposed Stochastic Error Ascent (SEA) combines semantic similarity retrieval and error propagation graphs to iteratively identify model errors. The framework is evaluated on several popular open- and closed-source large language models. Compared with baselines such as Automated Capability Discovery and AutoBencher, SEA demonstrates significant improvements in both the number and efficiency of discovered knowledge errors.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "+ SEA combines stochastic optimization, semantic similarity retrieval, and error graphs for automated knowledge deficiency discovery.\n+ Given a knowledge base, SEA is superior in the number of discovered errors.\n+ SEA requires no access to model internals, and can be readily applied to closed-source API environments.", "reasons_to_reject": "+ The SEA framework relies on the initial batch for error detection, and its iterative, similarity-driven search may inherit biases from this initial batch. While the authors mention a uniform retrieval strategy for the initial batch, important implementation details and a deeper discussion are lacking. It remains unclear whether all models use the same initial batch in all experiments, how each model performs on the initial batch, and more importantly, how sensitive the experimental results are to the choice of initial batch.\n\n+ The paper lacks a quantitative and systematic analysis of the diversity and the spread rate of errors discovered by SEA, making it unclear whether SEA identifies a broad range of error types or merely concentrates on a few clusters.\n\n+ Only comparing error counts with the ACD baseline may be unfair, since ACD tends to discard proposals that are similar to existing tasks, whereas SEA searches for errors similar to previously discovered failures. In addition, the budget comparison in the paper does not take into account the computational costs of knowledge base retrieval and embedding, which may underestimate the actual overhead of SEA.\n\n+ The explanation of some details of the method is not clear enough. The Random Sampling in Figure 1 is not mentioned in Section 3 and is only briefly described in Section 4, without explaining the purpose or necessity of doing so.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jtByYvQO4p", "reviewer": "Reviewer_PkVq", "summary": "This work proposes SEA, a scalable and efficient framework for discovering knowledge deficiencies (errors) in closed-weight LLMs under a strict query budget. it iteratively retrieves new high-error candidates by leveraging the semantic similarity to previously observed failures. SEA can uncover more knowledge errors from LMMs than the existing method Automated Capability Discovery and AutoBencher, while substantially reducing the cost-per-error.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This work presents a novel methodology for probing the capability boundaries of large language models (LLMs), with particular focus on identifying potential knowledge errors. The research demonstrates well-defined objectives and yields remarkably encouraging results.", "reasons_to_reject": "Some details need to be further clarified: \n1.\tDAG Modeling Rationale: Why is a Directed Acyclic Graph (DAG) used to model the entire decision process? What principles govern the edge construction in the DAG?\n2.\tInitial Seed Selection: How are the initial seeds selected? Does seed selection have impact on the final results?\n3.\tExperimant details: How to construct the experimental dataset? What is the specific schema/structure of the experimental dataset? How do different threshold values quantitatively impact error detection rates?", "questions_to_authors": "Providing a case analysis to visually demonstrate the objectives, process, and possible outcomes of the task.", "extra_scores": {}}, {"review_id": "YtH0btqnkU", "reviewer": "Reviewer_cJFu", "summary": "The paper \"Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base\" introduces Stochastic Error Ascent (SEA), a novel framework for uncovering factual weaknesses in large language models (LLMs), particularly those with closed weights. Existing benchmarks for evaluating LLM factuality are limited in scale and static in scope. SEA addresses this by formulating error discovery as a stochastic optimization problem under a strict query budget. Instead of exhaustive probing, SEA iteratively identifies error-prone knowledge regions using semantic similarity to past model failures. It employs hierarchical document-paragraph retrieval and a relation-directed acyclic graph (DAG) to model error propagation and prune low-yield sources, maximizing discovery efficiency.\n\nEmpirical results show that SEA discovers 40.7× more errors than Automated Capability Discovery and 26.7% more than AutoBencher, while drastically reducing the cost per error. Human evaluation confirms the high validity of SEA-generated questions. Further analyses reveal consistent intra-family failure patterns and overlapping model-specific deficiencies, especially in domains like history, culture, and science. SEA's adaptive querying and systematic diagnosis highlight its potential to guide future LLM development via targeted fine-tuning and enhanced data coverage. This work establishes SEA as a scalable, efficient alternative for uncovering hidden vulnerabilities in LLMs.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Novel and Scalable Framework\nThe proposed Stochastic Error Ascent (SEA) is a novel contribution that frames knowledge deficiency discovery in LLMs as a budget-constrained stochastic optimization problem, a formulation not previously explored in this domain. SEA’s approach enables efficient, scalable probing of massive knowledge bases without requiring full model access or exhaustive sampling.\n\n2. Empirical Superiority Over Baselines\nSEA demonstrates substantial improvements over existing methods:\n\n40.7× more errors discovered than Automated Capability Discovery (ACD).\n26.7% more than AutoBencher.", "reasons_to_reject": "Dependence on Black-Box Models and External Generators\nSEA relies heavily on an external LLM (e.g., GPT-4o) for question generation and evaluation. This introduces a dependency that can obscure the true deficiencies of the tested models, especially if the generator itself has biases or blind spots. It raises concerns about evaluation leakage and the circular use of LLMs to evaluate LLMs.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rXud6cvqUY", "reviewer": "Reviewer_UD1R", "summary": "The authors propose stochastic error ascent (SEA), an efficient framework for discovering knowledge deficiencies in closed-weight LLMs under a query budget. SEA iteratively retrieves new high-error candidates by leveraging the semantic similarity to previously observed failures, and employs hierarchical retrieval across documents driven by a relation directed acyclic graph-based error propagation method. SEA discovers knowledge deficiencies of various LLMs, showcasing their diverse vulnerabilities.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Interesting topic of dynamically detect LLMs' failure with an evolving evaluation set.\n2. Well-written paper and extensive experiments. I also like the figures.\n3. The proposed method is useful and it discovers interesting findings in understanding various models.", "reasons_to_reject": "Some technical details are not clear. Please see \"Questions To Authors\".\nI will also carefully read other reviewers' comments and authors' rebuttal to have a more accurate judgment.", "questions_to_authors": "Detailed Questions:\n1. I am wondering, if you randomly select your first batch of passages, will the variance of your method be very high? I didn't see any analysis about the influence of starting SEA from different initial passage groups. Can you tell me more about it?\n2. Why $\\xi=\\gamma$? At each step t, you will not include any new passage whose error rate is $< \\xi$, meaning that you won't prune any passage already in your source set, right? Also, if you compute the cumulative error for a source passage's descendants, when a new passage is include into the source set, it has no descendant, then how do you deal with them? I think this part is rather unclear and confusing. I hope more discussion can be added. Maybe you could provide an example in the rebuttal.\n3. I wish to see more analysis about various values of $\\xi$ and $\\gamma$. I think it may be some variant changing the observation.\n4. I don't know what you mean by \"we set the budget as 20,000 API calls for fclose plus the cost for QA generation\". This doesn't seem a clear description of budget. How much will it cost for QA generation in SEA and ACD? What if each API call asks for different amount of information? \n5. Similarly, what do you mean by \"and the budget as AutoBencher to generate 13 benchmarks when comparing with AutoBencher.\" Can you quantify that? Since the efficiency is one of your main claimed contribution. I think it is important.\n6. What version of wikipedia are you using? Knowledge might be outdated if you use previous versions of knowledge base. It might have a conflict with models' parametric knowledge.", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes Stochastic Error Ascent (SEA), a framework for efficiently discovering knowledge deficiencies in large language models (LLMs) under a limited query budget. SEA combines semantic similarity retrieval and graph-based error propagation methods to iteratively uncover model deficiencies without accessing internal weights. It significantly outperforms baselines in error detection and cost efficiency, though some concerns remain about bias, error diversity, and implementation details.
Decision: Accept