Copy-Paste to Mitigate Large Language Model Hallucinations
Authors: Yongchao Long
Yingying Zhang
Xianbin Wen
Xian Wu
Yuxi Zhou
Shenda Hong
Venue: ICLR 2026 Poster (2026)
Keywords: Hallucination
Context Learning
Contextual Faithfulness
Knowledge Conflict
Model Interpretability
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose Copy-Paste, a paradigm embedding contextual fragments for faithfulness, instantiated as CopyPasteLLM—achieving 12.2-24.5% accuracy gains with only 365 samples (1/50th of baseline) by recalibrating parametric knowledge.

Abstract: While Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to generate contextually grounded responses, contextual faithfulness remains challenging as LLMs may not consistently trust provided context, leading to hallucinations that undermine reliability. We observe an inverse correlation between response copying degree and context-unfaithful hallucinations on RAGTruth, suggesting higher copying degrees reduce hallucinations by fostering genuine contextual belief. We propose Copy-Paste, a generation paradigm that directly embeds contextual fragments to ensure faithfulness, and instantiate it through CopyPasteLLM via two-stage high-copying preference training. We design three prompting methods to enhance copying degree, demonstrating that high-copying responses achieve superior contextual faithfulness and hallucination control. These approaches enable a fully automated pipeline that transforms generated responses into high-copying preference data for training CopyPasteLLM. On FaithEval, ConFiQA and PubMedQA, CopyPasteLLM achieves best performance in both counterfactual and original contexts, remarkably with 12.2\% to 24.5\% accuracy improvements on FaithEval over the best baseline, while requiring only 365 training samples—1/50th of baseline data. To elucidate CopyPasteLLM's effectiveness, we propose the Context-Parameter Copying Capturing algorithm. Interestingly, this reveals that CopyPasteLLM recalibrates reliance on internal parametric knowledge rather than external knowledge during generation. All codes are available at https://github.com/longyongchao/CopyPasteLLM

Published as a conference paper at ICLR 2026
COPY-PASTE TO MITIGATE LARGE MODEL HALLUCINATIONS
LANGUAGE
Yongchao Long
Yingying Zhang
Xianbin Wen' Xian Wu
Yuxi Zhou
Shenda Hong*.
"Department of Computer Science, Tianjin University of Technology, Tianjin, China NNatioaal Institute of Health Data Science, Peking University, Beijing, China
Teencett Jarvis Lab, Shenzhen, China i Corresponning author
ABSTRACT
While Aarrival-AAumenter Generation (RAG) enables large language models (LLMs) to generate contextually grounded responses, contextual faithfulness re- mains challenging as LLMs may not consistently trust provided context, lead- ing hallucinations that undermine reliability. We observe inverse correla- tion between response copying degree and context-unfaithuul hallucinations on RAGTruth, suggesting higher copying degrees reduce hallucinations by foster- ing genuine contextual belief. We propose Copy-Paste, a generation paradigm that directly embeds contextual fragments ensure faithfulness, and instantiate it through
via two-stage high-copying preference training. We design three prompting methods to enhance copying degree, demonstrating that high-copying responses achieve superior contextual faithfulness and hallucination control. These approaches enable a fully automated pipeline that transforms gen- erated responses into high-copying preference data for training On FaithEval, ConFiQA and PubMedQA, CopyPastel.LMM achieves best perfor- mance in both counterfactual and original contexts, remarkably with 12.2% to 24.5% accuracy improvements on FaithEval over the best baseline, while re- quiring only 365 training samples-1/50th of baseline data. To elucidate Copy- PasteLLM's effectiveness, we propose the Context- Parameter Copying Captur- ing algorithm. Interestingly, this reveals that CopyPasteLLMM recalibrates reliance on internal parametric knowledge rather than external knowledge during gener- ation. All codes are available at httpt::///// //gi thub. com wow..ooooooggccccco/ LLP
INTRODUCTION
Large language models (LLMs) have brought revolutionary breakthroughs natural language pro- cessing (Annepaka & Pakray 2025 Qin et al. 2024),, while -errevall-augenntee generation (RAG)
further empowers LLMs with grounded external knowledge capabilities (Fan
2024
Zhao
ct al. 2024). However, LLMs inevitably suffer from knowledge conflicts (Xu et al.) 2024) when internal parametric knowledge conflicts with external contextual knowledge, LLMs may favor ternal parametric knowledge, leading to contextual faithfulness hallucinations (Bi et al. 2024 Ming
al. 2025 Niu et al. 2024). Such hallucinations are particularly critical knowledge-intenee
domains (Vishwanath et 2024) like rare disease medical consultations (Reese et al., (2025), where clinicians may lack systematic knowledge reserves (Zhang et al.. 2022) to judge whether model re- sponses are faithful to contexts, while patient communities often rely on self-consultation
LLM queries without professional medical supervision (Busch et al. 2025 Aydin et al. 2025). Chen & Shu (2024); Zhang al. (2025c) shows LLM-ggnerated content is more deceptive than human- written content. Without clear attributability, faithfulness hallucinations pose potential risks to clin- ical decisions and patient behaviors (Kim et al. 2025).
Current research primarily follows two directions in enhancing the reliability of LLMs: (i) gener- ation with citations, where models produce responses accompanied by attributable eitations (Wul

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Published as a conference paper at ICLR 2026
al. 2025
Abolghasemi et al. 2025 Ji et al.
2025 Press et al.
2024
Song et al. 2025 and
(i) improving contextual faithful ness through techniques such as prompting strategies (Zou et al,
2023
Zhang al. 2025a), constrained decoding (Shi et al. 2024
T.y.s.s et al. 2025 Liu et al, 2025 Li et al., 2025a). How-
2025
or fine-tuning Bi et al. 2025 Huang et al. 2025b Si et al.
consistency between the generated content and its cited sources, while the latter typically lacks mechanisms for explicit attribution. Consequently, achieving both faithfulness and verifiable attribution remains critical and unresolved challenge.
ever, the former struggles to
0.4.6 0,5
Internal parametric knowledge
Sentence
Base: Galleoo Galliei responsible Galleo's Law
External contextual knowledge
Clause
of Inerbia, which states that an object move with constant velocity...
Copy Coverage Copy Densiyy
Query
Explicit attribution
arower wio contect
Counterfactual Context ()): Gallies Galile,, renowned... One of Galleo's lesser known we achievements is his deoelopment of the Three nas of Motion which were citical acvancing study kinematies and dynamics, these articulate
which law was Galiieo Galilei responsible for describing?
of inorio, reletonship between fors, ad motion and the law of action and reacioon. In contrast to Newton's leter contriburke Gallico's auticulation the Three lawr Mozion war pintal in traostion
Answer the question with absolute fidelity
0.25 0.76
Atributed: Geilleo Gallei was not responsible for Bescribing the Three laws of Motion was
Copy
Copy-Paste (Ours)
Newton Three Motion
Copying Degree: K 0.3.5 12.78
= 0.48,5
Salleo Gallei responsible for desci laing the Thees Laws of Motior
Citation: Gaileo Calliei not responsible, ... The
the
Three deser bee by Sir Ioaac Newron
[1]
was faa from Arstttellinn plasics Newto taan mech aarics
However Ga Ico Galllei contr bute
Haama2-7bbccht
11mma--13b.chhtt
400
1455
1140
Figure 1: Upper: Response composition patterns comparison between Copy-Paste and mainstream approaches. Lower: Inverse correlation between copying degree and faithfulness hallucination show hallucination.
across different models. Kernel show copying degree; Bar
To address these challenges, we propose an intuitive solution: rather than having models reinterpret retrieved content, we advocate for directly quoting original sentences. This copy-paste generation strategy embeds key contextual fragments directly, avoiding secondary knowledge processing and potentially reducing paraphrasing hallucination risks. Importantly, copied content itself serves as direct evidence faithfulness without requiring additional verifiable attribution mechanism. This approach is motivated by observation of an inverse correlation between copying degree and hal- lucination density on the RAGTruth dataset (Figure leading us to hypothesize that high copying degrees may help mitigate hallucination problems.
Specifically, we formally propose Copy-Paste as generation paradigm that leverages high-copying degree operational proxy for contextual faithfulness through a two-stage pipeline that inter- nalizes surface-level copying behavior into model-level contextual trust. The first stage generates high-copying responses through hard and soft constraints enhance copying degree. The sec- ond stage (CopyPasteLLM, applies direct preference optimization (Rafailov et al. (202)) training internalize the high-copying preferences from the first stage into the L.M'' contextual faithful- ness. Experimental results demonstrate that CopyPasteLLMM, trained on only 365 high-copying sam- ples, outperforms strongest baselines by 12.2%-24.5%% on EvilhEval. Additionally, we propose the Copying Capturing algorithm, which enables fine-grained analysis of knowl- edge source reliance throughout the entire Shain-of-Thoughh reasoning process, rather than merely examining final short answers. The algorithm captures contextual versus parametric knowledge usage at each token position, providing novel insights into how models dynamically balance differ- knowledge sources during sequential reasoning. Mechanistic analysis reveals CopyPastel.IMM
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maintains similar contextual knowledge representations the base model while recalibrating inter- nal confidence in parametric knowledge, thereby enhancing contextual trust.
PRELIMINARIES
2.1 PROBLEM FORMULATION
Task Given query Q and context C, the model generates an answer A. In high-stakes domains such as medicine, the faithfulness of the generated answer to the context is of paramount importance. While conventional RAG research often emphasizes abstractive generation and semantic relevance, our focus in this work is specialized task that we term Copy-Paste. The goal of Copy-Paste is to maximize the reuse of lexical units from the context C' in the final answer A, thereby ensuring high contextual faithfulness and minimizing hallucination. Formally, the task can be defined (Q, C) + A.
Quantification Following Grusky et al. (0018) we quantify the response copying degree from context with metrics:
Σ 1fl. JEF
8
[A| Σ 1f?
A
where is is the set of copy fragments computed by copy fragment detection algorithm (detailed Appendix[].. D. denotes sequence length. Copy Coverage (x): the fraction of answer tokens that are covered by some copy fragment, reflecting the overall degree of lexical reuse. Copy Density (d): a sensitive variant that emphasizes longer copied fragments, capturing whether the answer tends to copy long spans verbatim rather than isolated words.
Balance While maximizing copy-paste is central to our formulation, an effective answer A should also remain relevant the query Q and be linguistically fluent. Specifically, measure query relevance using embedding-basee similarity, and fluency via perplexity. Thus, the Casy-Paste task be viewed optimizing trade-off among faithfulness, query relevance, and fluency. Un- like extractive summarization (Zhang et al. 2023). Copy-Paste is query-aware and ensures fluent, context-faithfin answers.
2.2 MOTIVATING OBSERVATION ON RAGTRUTII
To validate the intuition that high copying degrees may reduce hallucination, we conducted prelim- inary analysis on the RAGTruth QA subset et al. (2024), which contains 839 context-dependent questions. Each question includes responses from 6 different models with word-level contextual faithfulness hallucination annotations, enabling precise quantification of hallucination density per model.
We computed copy coverage (k) and copy density (8) for each model's responses across the dataset, then visualized the relationship using two-dimensional kernel density estimation with copy coverage (x-axis) and copy density (y-axis). The analysis reveals clear pattern: density kernels positioned toward the upper-right region (indicating higher copying coverage and density) correspond to lower hallucination density across models (Figure
METHODOLOGY
Our approach consists two sequential stages: (1) constructing high-copying candidate responses through Copy-Paste-Promppine methods, and (2) training CopyPasteLLM through automated pref- erence data construction that internalizes a preference for contextual evidence. Figure 2 illustrates the complete pipeline. To verify that the learned policy truly reallocates reliance from paramet- ric priors to context, we additionally introduce an interpretability tool, Context- Context-Paramette Copying Capturing.
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Stage 1: Constructing High-Copying Responses
hard-constraint
CP-Order
Selec eeeet the sentences from context
Reorder the santences
coherence
Or generate some senterices senterices
CP-Link (p)
soft- cofstronstraint
Whiter
Reviawer
Sufficient x and
C
Sufficient Query Relevance ?
CP-Refine
BB Sufficient Context Faithfulness
Input: Responses of CP-Order, CP-Link, CP-Refine, Base, Attributed and Citations
Stage 2: Internalizing Contextual Trust from High-Copying Preferences
Multi-CCittriia
2. Hallucinations
3. Stamping Answers
Co.yyiing Preferences Alignment
Filtering
Tournament
Basa Ciarons
Stamping
First (Cheson)
Base
Attributed
CopyPasteLLM
Stamping Wronng
Citations
Chasen Candidates
ELO Ranking
Other Candidates
Proferance Paiirs
Figure 2: Two-stage Copy-Paste pipeline: Stage constructs high-copying responses; Stage 2 filters, judges, stamps answers, and aligns preferences to train CopyPastel.LMM.
3.1
CONSTRUCTING HIGH-COPYING RESPONSES
We operationalize the Copy-Paste objective through three complementary prompting paradigms that progressively relax constraints while preserving lexical fidelity to the context. CP-Order implements strict extractive regime: it first selects context sentences relevant the query and then directly reorders them into a coherent answer. This hard constraint intentionally forgoes abstractive para- phrasing, which suppresses the model's tendency resolve conflicts using parametric priors. The method excels when answers can be composed from a small set of highly informative sentences but tends sacrifice fluency when discourse connectives are missing. (See L.1.1 L.1.2 for prompts)
CP-Link maintains the same extractive core but allows the model to generate short transitions be- tween copied spans. These transitions are not intended introduce new facts; instead, they serve as discourse glue to restore local coherence after sentence reordering. Empirically, this limited gen- erative freedom improves readability while preserving the high-copying signature that anchors the answer to source text. [..e L.3 for prompts)
contrast, CP-Refine adopts soft-constraint, iterative refinement process with a writer-reviewer loop. The writer proposes answer given the query and context; the reviewer provides verbal feed- back focused on copying degree, contextual faithfulness, query relevance, and fluency; the writer then revises the answer until composite copy copying as a target state that is continually optimized rather than a fixed structural constraint. As shown by our experiments (See Table 2) CP-Refine achieves better balance among faithfulness, readability, and relevance (See|L.1.4[for prompts). Algorithm[]]i Appendix summarizes the unified procedure, which stream preference construction.
exceeds threshold. This procedure treats
to produce diverse yet consistently high-copying candidates for down-
3.2 COPYPASTELLM: INTERNALIZING CONTEXTUAL TRUST FROM HIGH-COPYING PREFERENCES
Copy-Paste-Prompping supplies not only single responses but a structured spectrum of behav- iors-from strictly extractive to softly refined. CopyPastel converts this spectrum into explicit
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preferences that can be internalized by a policy through direct preference optimization. Our pipeline begins by generating six types of candidates for each query-context pair: conventional abstrac- tive baselines (Base, Attributed, Citations) and three Copy-Paste variants (CP-Order, CP-Link, CP- Refine). We then perform multi-criteria filtering that simultaneously enforces contextual faithful- ness (AlignScore, MiniCheck), copying strength (k, 6), query relevance (embedding similarity), and fluency (perplexity). This step ensures the retained set covers high-quality front of the faithful- ress-flueney-relevanee trade space rather than merely maximizing copying.
The remaining candidates are ranked by an Elo-style LLM-as-Judge tournament that diagnoses two major hallucination Toides-Twist and Causal so the final preference reflects error severity, not only stylistic quality. A key nuance arises when gold answers are available: we append the correct answer the top Copy-Paste candidate transform faithful reasoning into a definitive conclusion, while appending incorrect answers to the other Copy-Paste candidates to create informative nega- tive pairs. This labeling strategy focuses learning on trusting context while disentangling reasoning traces from final decisions. The resulting dataset yields roughly five preference pairs per sample, en- abling data-efficient DPO training that teaches the model to prefer high-copying, context context-grounded responses even when they conflict with parametric priors. Algorithm in Appendix formalizes the procedure.
3.3 CONTEXT-PARAMEE COPYING CAPTURING
Context- Parammter Copying Capturing provides principled, token-level probe of knowledge usage during generation. The method executes runs for each query: with context and without context. At each decoding step in Shain-of-Thoughee mode, it collects the top-K candidate tokens with their probabilities and hidden states. Tokens that appear in the provided context taken contextual knowledge, whereas tokens that are preferred the context-free run serve as proxies for parametric knowledge. Algorithm specifies the full procedure.
Conceptually, this procedure is inspired by Knowledge Token Capturing (KTC) Bi et al. 2024).
Unlike KTC, which primarily analyzes short final answers, Context-Parameter Copying Cap- turing extends the analysis to the entire Chain-of- Chain-of-Thought response trajectory, enabling sequential, position-awes aware assessment of contextual versus parametric reliance.
EXPERIMENT
Our Copy-Paste approach is a two-stage framework where Sooy-Paste-Promptinnn generates high- copying preference data, and LMpyPasteLLLM learns contextual faithfulness from this data. To val- idate our complete pipeline, we conduct comprehensive experiments addressing three key research questions:
RQI: Do Copy-Paste-PPe ProstePrommting methods effectively enhance contextual faithfulness and mitigate RAG hallucinations through high-copying response generation?
RQ2: Does training with high-copying responses from Copy-Paste-Promppinn as DPO preference trajectories enable CatyPasteL.LMM to genuinely trust contextual knowl- edge evee-even
even when it is counterfactual?
RQ3: What are the underlying mechanisms of CopyPasteLLM's contextual belief? We will interpret this by analyzing logits and hidden states.
4.1 TWO-STAGE FRAMEWORK VALIDATION
Experimental setup is detailed in Appendix B
4.1.1 STAGE 1: COPY-PASTE PEA---ASSSTPPIIIINN PROMPTING PREFERENCE DATA GENERATOR (RQ1)
In the first stage, we evaluate whether our prompting methods can effectively generate responses with high-copying and improved contextual faithfulness. The baselines here represent different sponse generation paradigms that will serve as rejected responses our LopyPasteLLM training. Our primary objectives are to: (1) validate that Paay--aatt-PPompiinn methods achieve superior
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Table I: Counterfactual scenarios: Performance comparison of CopyPasteLLM against baselines. We removed 241 samples used for training CopyPasteLLLM from FaithEval, with the remaining samples used for testing (detailed in the RQ2 setup of Appendix Table 4 Training size column shows the amount of training data for fine-tuning-batt based methods. T indicates seen data for the respective model. Bold values highlight the best performing method unseen settings.
Training Size
EvithEval
ConFiQA-QA
ConFiQA-MR
ConFiQA-MC
Model
Method
Acc
Hii
Acc
Hit
Acc
Hit
Acc
Hit
Context-DPO (B) 2025
18,000
80.2
36.7
88.97
96.1
88.4"
85.8
92.17
80,97
Lians-
3.8D
CoColes Lx
Attributed Zhou ci
2024
2125
67.1
69.2
34.2
17.9
51.5
48.5
91.4
37.4
53.9
53.3
71.5
14.8
37.3
36.1
53.6
15.5
Canoe Si at
2025
10,000
71.4
34.0
64.3
93.2
66.6
83.8
64.5
73.7
ParamNuue Huang 0
20250
32,580
68.5
22.5
74.4
82.2
75.5
72.4
81.4
70.2
CopyPastel (Ours)
Mistral-
7D v0.2
Context Bi ml
2025
18,000
77.1
33.8
84,8
94.8"
81.3
85.3
80.4
80,8
Atrributed Zhou et
CoCoLex T.3.5
2025
65.3
35.4
57,3
50.8
41.8
33.5
32.5
33.7
CopyPaste
(Ours)
365
89.3
41.8
84.4
95.0
80.8
90.8
82.5
86.3
3.1 3.1.8
Attributed Zhou et al
2023
65.5
32.0
49.9
88.4
39.8
69.2
15.5
$2.6
CoCoLex 11333.3
68.1
36.2
CopyPasteLLLM (Ours
365
92.6
41.0
72.4
90.1
75.4
84.8
83.5
79.9
contextual faithfulness through explicit copying mechanisms, and (2) generate high-quality pre- ferred responses for subsequent DPO training. A comprehensive comparison with sate-of-theeart methods will be presented in the next stage after DPO training.
Table 2: Performance comparison of Copy-Paste- Paste-Prompting against baselines across models and datasets. Methods with colored backgrounds are our proposed Copy-Paste-Prree Prompting. Bold indi- cates the best performance, underlined indicates the second-best performance. Faith.: Faithfulness (M. C.: MiniCheck, A.S.: AlignScore), Hallu.: Hallucination, Flu.: Fluency.
RAGTruth
FaithEval
PubmodQA
AVERAGE
Method
Faith.
Hallu.
Fin.
Faith.
Hallo,
Faith.
Hallo.
Flu.
Faith. Hallu. Flu.
MLC.
A.S.
Twist Causal
M.C.
A.S.
Twist Causal
M.C.
A.S.
Twist Causal
Mhetral-7B-lnttms--0022222 (71)
Ammibuted
69.58
63.43
1506.
1494.5 19.54
38.28
90.67
1827.1 1513.7 37.32
75,49
77
90
1464.7
1450.4 23.53
77.56
1492.9 26,80
Citations
1475.7
1415.
1370.0
1423.7
1518.9
1519.5
93.41
1521.9
1580.7
1528.4
CPettder
91.25
71.98
1467.9
14724 63.62
94.89
92:27
1522.6
150LS 43.74
93.18
82
15283
1559.1 32.65
87.65
1508.5 47.54
CP "P-Refine
82.18
74.56
15338
1537.9 18.46
92.85
94.65
1547.4
1546.7 26.63
91.52
58.21
1572.7
1519.7
12.79
87.35
1546.4 20.96
Liama
3.1 SB Instruct (8B)
Auributed
57.02
65.29
1526.3
1554.3
26.22
85.22
85.65
1516.5
1536.9 330.8
71,10
60.01
1530.0
1553.1
47.36
70.72
1536.2 134,8
72.81
1429.5
1403.4
1463.4
77.38
1485,5
CI Limk
20:58
78.83
1401.1
1328.3 17.83
91.54
89.23
1456.2
1366.3 24.09
80.74
80.79
1396.1
1371.1 19.65
$1.95
1386.5 20.52
CP-Deder
75 75.30
94,81
1498.4
1498.0 26.35
95.44
98.12
1523.2
1541.2 33.46
87.07
97.62
16336
1559.1
27.83
91.39
1542.5 29.21
Ketine
77.30
1645.7
94.40
26.99
87.29
1536.3
1553.2
98.74
1549.7
Qwen2
5-228-nnite (728)
Altributed
1304.5
1501.5
Citations
77.52
1455.5
1498.0
18.61
90.98
1476.7
34.67
82.01
1358.8
1413366
22.80
81.63
1443.2 25.39
2 pins
75.75
85.37
1446.3
1363.2
27.47
92.88
92.00
1443.5
1424.2 39.55
86.21
88.3
1527.9
1480.2 39.43
86.80
1449.1 31.48
CP-Ori Ordder
76.32
94.60
1509.2
1589
30.56
95.78
98.16
1539.3
1579.7
38.11
87
97.52
1546.8
1575.9
35.26
91.71
1556.8 34.65
CP-Refine
1584.6
27.65
1556.7
1579.9
1549.6
DeepSeck:
03-00324 (671B)
Ammibuted
59.60
1417.1
1449.1
36.90
63.27
09
1449.2
1487.9
71.86
1477.1
Citatoos
62:32
64.45
1510.8
1565.6
34.63
$7.38
85.09
14633.0
1477.0 36.09
75.93
71.85
1450A
1387.5
23.27
74.60
1477.4 31.33
C1 pank
70.59
72.54
1382.9
[360.] 34.19
92.60
88.08
1489.
1374.8 35.55
81.56
73.8
1380.9
1351.1 28.54
$0.51
1389. 32.76
92.87
1579.4
1555.1
95.23
1569.9
34.30
1621.7
91.00
90:02
1609.8
94.45
1651.7
1583.0
Our experimental results demonstrate that Copy- Peayy-Pate-Preeeee PassteProomping methods consistently outperform baselines across all evaluation metrics (Table 2). (1) CP-Refine excels in hallucination reduction (best in 3/4 models, 14/24 top scores) and contextual faithfulness (+10.9% to 19.1% over base- lines) while maintaining fluency-achies achieving best perplexity in Q-72B/D-V3 and second-best in M- 7B/L-8B, suggesting advanced models better handle high-copying constraints. (2) CP-Order leads contextual faithfulness (14/24 top scores) with second-best hallucination performance but notably poorer fluency. (3) CP-Link shows modest improvements excelling only in contextual faithfulness

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Published as a conference paper at ICLR 2026
with even worse fluency than CP-Order, indicating hard constraints limit generative capabilities. (4) We observe strong cetuuciinatio-aaitulllttee correlation: in 18/24 scenarios (75%), optimal hallucination performance coincides with best contextual faithfulness. We hypothesize that the supe- rior contextual faithfulness of SepyyPaste-Prompee Peste-Prompting achieves significantly higher copying degree than the two baselines (see Appendix Figure 5). Additionally, we compare query relevance between the three Copy-Paster Sopy-Paste-Prompee Prompting methods and the strongest baseline in Appendix Figure [6] demonstrating that Peay-Paste-Reiinng can address queries while maintaining high copying rates through soft constraints.
stems from high-copying in responses. Copy-
4.1.2 STAGE 2: COPYPASTELLM (RQ2)
Accuracy in non-counterfactual settings. PubMedQA is evaluated on artificial subset 20,000 samples (none used for CopyPasteLLM training, see Appendix Table 4 ConFiQA uses Original context and Original answers.
Table 3:
-Bstral-7B-v0..2
Llama-3-8B
Llama-3.1-88
Method
PubMed QA
ConFIQA
PubMed
ConFIQA
PubMed QA
ConFiQA
QA
QA
MR
MC
QA
MR
MC
QA
MR
MC
Base LM (Ours)
88.60
96.22
71,20
72.27
97.3
98.02
93.00
91.02
98.15
97.93
89.48
89.97
90.26
91.40
97.43
91.87 91.20
97.5
99.30
97.17 96.27
97.67
99.02
94,95 94.92
95.73
CopyPasteLLM demonstrates remarkable efficiency by achieving superior performance counter- factual scenarios using only 365 query-context pairs as input to construct preference data through our automated pipeline-a a base data requirement that is 50x smaller than the strongest baseline Context-DPO (18,000 samples) and significantly more efficient than other fine-tuning methods such as Canoe (10,000) and ParamMute (32,580). As shown in Table [ on the FaithEval coun- terfactual subset, LM surpasses the strongest baselines by substantial margins: 12.6, 12.2, and 24.5 percentage points across Llama-3-8B, Mistral-7B-v0.2. and Llama-3, respec- tively, achieving peak accuracy of 92.8% on --ama-3-8B-remartt reported 47.5% on this challenging subset (see Appendix Table 6 Additionally, Copy PopyPastLLMM consistently achieves the highest Hit Rate across all models, despite the inherent difficulty of exact matching in FaithEval's lengthy gold standard answers. On ConFiQA's three counterfactual subsets, CopyPasteL.MM maintains superior performance unseen settings compared to recent fine-tuning baselines and copy- copy-guided decoding method CoCoLex, with particularly notable results on Mistral- 7B-v0.2 where it outperforms even Context-DDD trained on ConFiQA on the most challenging Multi-Conffict subset.
outperforming GPT-4o's
In non-counterfacttal scenarios, CopyPasteLLM maintains exceptional contextual faithfulness while demonstrating significant improvements over base models (Table B) On relatively straight- forward datasets Paausett-PuMMMdOO. and ConFiQA-QA SAAFiQAA-A-ttte method achieves modest but consistent im- provements, with average accuracy gains of 1.01 % (from 96.04% to 97. .0.05.)).......))))) More importantly, on the more challenging COR and ConFiQA-MC subsets, CopyPasteLLMM delivers substan- tial performance gains, improving average accuracy from 84.49% to 94.37%, with the most dramatic improvement of 20.67% observed on Mistral-7B-v0.2. for the MR subset. These results demonstrate that CopyPasteLLMM s enhanced contextual trust, achieved without introducing additional parametric knowledge through LoRA training, leads to significant improvements in wwwwledge-intesiie ques- tion answering accuracy. For fine-grained analysis across conflict complexity, knowledge domains, and reasoning ambiguity, see Appendix DL for response length and copying behavior analysis, see Appendix F. for ablation studies and training dynamics, see Appendix G
4.2 INTERPRETABLE ANALYSIS OF COPYPASTELLM (RQ3)
We propose the Context-Parameter Copying Capturing (Algorithm which is designed to capture the degree to which the model copies contextual or parametric knowledge during token generation. Specifically, in CoT reasoning mode, our method monitors the model's internal representations by analyzing the top-K token logits (ranked by probability) and corresponding hidden states at each generation step. thereby quantifying the model's reliance external context versus internal para- metric knowledge. This algorithm extends the Knowledge Token Capturing (Bi et al., (024)) to sequential analysis, enabling comprehensive evaluation of model responses during CoT reasoning.
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Copy-Paste
Base CTX
Copy-P asle Para.
Base
Mistral-78-Instruct--02 Samples
1amm-3.1-88-Instee
Samples
Lagits Power
RAGTTruth
200
-400
630
350
406/1000 (40.6% Samples
532/1000 (53.2%) Samples
Logits Power
600
1000
400
FalthEval
200
85
170
255
340
425
132
176
220
570/1000 07) Samples
1500
554/1000 (55.4%) Samples
Logits Power
750
1000
PubMedQA
500
250
500
Response Length
Response Length
Figure 3: Logits power distribution across response lengths for contextual (CTX) and parametric (Para.) knowledge. Values above x=0 indicate CTX logits power, values below x=0 indicate Para. logits power (negated for visualization).
Copy-P
Copy-Paste Para.
Mistral-7B Cestral-7B-nnstuccee at-.inntrcct-0.3
Liama-3 3.1 a8-instrctt
Base Base Para.
Copy-Paste CTX Copy. aste Para.
Base Copy-Paste CTX
Para. Copy-Paste
Figure 4: Dimensionality reduction visualization of hidden states distributions between contextual (CTX) and parametric (Para.) knowledge on PubMedQA dataset across two base models. Each subplot shows pairwise comparisons with marginal KDE distributions and confidence ellipses. See Appendix Figures 15 and [4 for RAGTruth and EaithEval.
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We first analyze the logits output power of CopyPasteLLMM and its base models across three datasets at each generation step, considering both the magnitude and frequency of logits at specific response positions, illustrated in Figure To ensure fair comparison by providing base with longer to- ken generation opportunities, we filtered out samples where CopyPastel responses exceeded base response lengths, with complete dataset statistics shown in Appendix Figure [3] Our anal- ysis reveals three key observations: (1) In CoT with context task, Both base and CopyPasteLLMN demonstrate higher reliance on contextual knowledge than parametric knowledge. (2) However, CMPyPasteLLLM exhibits significantly stronger contextual knowledge utilization compared to base, while showing reduced reliance on parametric knowledge. (3) From a positional perspective, Copy- PasteLLM achieves peak contextual knowledge utilization earlier in the response generation process than base. Collectively, these findings suggest that CopyPasteLLLM not only demonstrates stronger but also earlier contextual engagement compared to base, indicating enhanced contextual trust and willingness to believe the provided context.
We further employ UMAP dimensionality reduction to analyze the captured hidden states distri- butions, as shown Figure Our visualization reveals two striking patterns: (1) Base models exhibit minimal distinction between contextual and parametric knowledge semantic representations (Ist column). whereas LM demonstrates relatively clear separation between these two knowledge types (2nd column). (2) More intriguingly, contextual knowledge representations in CopyPasteI_LMM remain nearly co-distributed with those in base models (3rd column), while their parametric knowledge distributions differ substantially (4th column). Based on these observations, we infer that CopyPasteLLMM fundamentally recalibrates the model's internal confidence in paramet- ric knowledge without compromising its contextual processing capabilities. This selective paramet- ric knowledge suppression, rather than contextual knowledge enhancement, enables achieve superior contextual faithfulness by strategically reducing competition from internal para- metric knowledge during generation. For theoretical interpretation of this mechanism through attention dynamics and entropy reduction, see Appendix A
RELATED WORK
While Retrieval-Augmentted Generation (RAG) has emerged as a promising paradigm for grounding large language models in external knowledge (Fan et al. 2024 Zhao et al., 2024), ensuring contex- tual faithfulness remains an open challenge. LLMs often exhibit a tendency to rely on their pre- trained parametric knowledge rather than adhering to the provided context, resulting in responses that may contradict or ignore retrieved evidence (Niu et al., 2024 Bi et al. 2024] Ming et al.) 2025). This contextual unfaithfulness poses significant concerns in critical applications such as health- care (Vishwanath et al., 2024| Kim et al. 2025), where accuracy and reliability are paramount.
Existing research has systematically studied this phenomenon from evaluation and mechanistic per- spectives. Evaluation studies construct synthetic scenarios revealing L.Ms' propensity to favor in- ternal knowledge over external evidence (Xu al. [024 Li et al. 2025b Joren al. 2025 Goyal et al. 2025). Mechanistic analyses identify attention heads (Wu et al. 2024 Huang et al. (025a),
FFNs (Sun 2024) and logit distributions (Bi et al. 2024) that respectively process external and internal knowledge sources.
Solutions to improve contextual faithfulness include generation with citations (Gao al. 2023 Press et al. 2024 Song et al. 2025 Wu et al. 2025), prompt engineering (Zhou et al. [2033] Zhang
jet al., 2025a). decoding methods (Shi et al.| 2024; T.y...s et al.) 2025: Liu et al. 2025) and fine- tuning (Bi et al. 2025 [Si et al.) 2025 Li et al. 2025a Huang et al. 2025b). While generation with citations methods may lack content-source consistency and other approaches often provide limited attribution mechanisms, our Copy-Paste paradigm targets both challenges simultaneously: enhances contextual faithfulness through direct lexical reuse from source text while inherently providing transparent attribution, and internalizes this copying behavior into genuine model-level contextual trust through preference optimization.
6 CONCLUSION
We propose Copy-Paste, a generation paradigm that directly embeds contextual fragments into re- sponses to mitigate faithfulness hallucinations in RAG systems. Based the observed inverse

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correlation between copying degree and hallucination density, instantiate this paradigm through a two-stage framework: Copy-Paste-PPe Prostt-no methods first generate high-copying responses, then preference optimization internalizes contextual trust into CopyPasteLLMM CopyPasteLLLM achieves remarkable data efficiency, delivering 12.2%-24.55% improvements on FaithEval using only 365 training samples-5tt 50x smaller than existing baselines. Our Context-Paramees Copying Capturing analysis reveals that effectiveness stems from recalibrating parametric knowledge confidence rather than enhancing contextual representations. The copy-paste paradigm provides an elegant solution to RAG attribution challenges, where copied content serves as inherent faithfulness evidence with- out requiring additional verification mechanisms. We discuss limitations and future directions in Appendix K
7 ETHICS STATEMENT
This work addresses the critical challenge of contextual faithfulness in large language models, par- ticularly in high-stakes domains such as healthcare. While our CopyPasteLLM approach aims to reduce hallucinations by promoting direct copying from provided context, we acknowledge poten- tial risks: over-reliance on copied content may lead to verbatim reproduction of potentially biased or incorrect source material. The method's effectiveness depends on the quality and accuracy of the provided context, and users should exercise caution when applying this approach in sensitive ap- plications. We encourage responsible deployment with appropriate human oversight and validation mechanisms.
8 REPRODUCIBILITY STATEMENT
To ensure reproducibility, we provide the following: (1) All experimental details and hyperpa- rameters are documented in the appendix. (2) We use publicly available datasets (FaithEval,, ConFiQA, PubMedQA, RAGTruth) with standard evaluation protocols (see Appendix B (3) Model training details, including DPO hyperparameters (see Appendix (D and preference data
construction procedures (see Algorithm [ and 2).
(4) The Context-Parament Copying Captur-
ing algorithm is fully described in Algorithm 4
(5) All prompting templates for Copy-Paste-
Prompting methods are provided in Appendix IL The complete implementation available at
https://githun http://hwww..oooooooooo///nnao/a
ACKNOWLEDGMENTS
We sincerely thank the anonymous reviewers and area chairs for their insightful comments and constructive suggestions that helped improve this paper. This work supported by the National Natural Science Foundation of China (62102008, 62172018, 62202332, 62776197, 62020106000 92048301), the CCF-Tencent Rhino-Bird Open Research Fund (CCF-Tencent RAGR20250108 the CCF-Zhipu Large Model Innovation Fund (CCF- CCFF-hhip2022414) the Tianjin Science and Technol- ogy Program (23JCYBJC00360), the Key Research and Development Program of Shaanxi Province --23-ZDLGY-488) the Tianchi Elite Youth Doctoral Program (CZ002701, CZ002707), the PKU- OPPO Fund (BO202301, BO202503), the Research Project of Peking University the State Key Laboratory of Vascular Homeostasis and Remodeling -2025-SKLVHR-YYO -LS--KKKLHH-YYTSS-022 and the Beijing Municipal Science and Technology Commission (2251100000025000))
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Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang. Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui. Retrieval-augmenner generation for ai-generated content:
A survey. CoRR, abs/2402.11473.. 2024. doi: 1044550/ARXIV.2400..oAA URL https: 48550 / rwv. 2402.1222229
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez. and lon Stoica. Judging lim-as-a-judge with mt-bench and chatbot arena. In Proceedings of the 37th International
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Conference on Neural Information Processing Systems, NIPS '23, Red Hook, NY, USA, 2023. Curran Associates Inc.
Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. Context-faithful prompting for large language models. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 14544-14556, Singapore, Decem- ber 2023. Association for Computational Linguisties. doi: 10.18633/1/20222 www.iiinggeeenll..o... URL wwwphttps://ee haaaacaaannoooooooooooooo wwwp:y/.......ooo..ooog. fin emn.- 9687
A MECHANISTIC INTERPRETATION OF COPY-PASTE EFFECTIVENESS
In this section, provide mechanistic interpretation explaining how the external constraint of high-copying responses translates into improved contextual faithfulness and reduced hallucinations within the L.M'' internal dynamics. This interpretation connects the Copy-Paste objective to fun- damental model components, which we analyze from two perspectives: (1) Attention Dynamics and Contextual Anchoring, and (2) Information Entropy Reduction.
A.1 ATTENTION DYNAMICS AND CONTEXTUAL ANCHORING
In Transformer-based architectures, the probability of generating the next token Yt is governed by the attention mechanism. Let the input sequence be the concatenation of the context and the generated prefix Ver. The attention output h, at step É is weighted sum of value vectors:
Σ V JEC
kEyer
Contexiual Atlention
Parametric Attention
where or,, represents the softmax-normalized attention weights. Hallucinations typically occur when the model fails to attend to the context (low Ejec at,j) and instead relies on internal para- metric priors activated by the generated history.
The Anchoring Effect: We posit that Copy-Paste Poste leverages the Induction Head mechanism (Olsson et al. 2022. Huang et al. 2025a), a circuit responsible for in-context copying. If we enforce the previous token 1 be direct copy of token o₁ C (where e the token position in the context), the query vector qe (derived from yt-1) will strongly correlate with the key vector k., in the context.
Mathematically, maximizing the copying likelihood in Stage 1 (Section effectively optimizes the attention weights such that:
score (o.) qe krg
> q
Kpam
This creates "Semantic Anchor," forcing the attention distribution a to collapse onto the context:
lim copying TaX
Σ
aaj 11
JEC
By ensuring Mt-1 is a copy, we mechanically guide the induction heads retrieve the subsequent ground-truth token Ck+1 from C, thereby physically suppressing the attention pathways that lead to parametric hallucinations. This aligns with our empirical observation Figure 3 where Copy- PasteLLM exhibits significantly suppressed parametric logits.
A.2 ENTROPY REDUCTION AND SEARCH SPACE CONSTRICTION
From an informatioonthhoortiicc perspective, faithfulness hallucinations arise from high uncertainty in the conditional distribution ( [C, yes). Let V be the full vocabulary of the LLM, and Ve c V be the subset of tokens present in the context.
The Copy-Paste objective imposes constraint that the generated response Y must maximize lexical overlap with C. This effectively serves as regularization term that constrains the search space Ω from the vast V the much smaller Vc-
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The conditional entropy of the generation step without constraints is:
H H(Y(C) Base
Σ WEV
(usee log Basse Buse(w)))
(5)
In standard generation, the probability mass often distributed over a long tail of semantically similar but extrinsically hallucinated tokens from parametric memory. In contrast, CopyPasteLLLM optimized concentrate probability mass on Vc:
Σ
Pcp(w]C) +1 = Per(w $ Vc) -
(6)
WE Ve
Consequently, the entropy of the Copy-Paste distribution is strictly lower than that of the base dis- tribution:
H(Y|C)cp < HC)
By minimizing the entropy and pruning the probability of tokens w & Vc, we statistically minimize the risk of sampling hallucinated content. This theoretical result explains why our method, despite relying lexical proxies, effectively improves semantic faithfulness by climinating the "lexical pathways" that allow parametric priors leak into the generation.
B EXPERIMENTAL SETUP
Datasets We evaluate across four QA datasets: RAGTruth (Niu et al. 2024), RAG hallucination
corpus with 18K word-level annotated LLM responses; FaithEval (Ming et al.
2025). counterfac-
tual benchmark for contextual faithfulness; PubMedQA (Jin et al.) 2019), a biomedical QA dataset
where contexts contain 21 % numeric descriptions; and ConFiQA (Bi al. 2025), which includes
both counterfactual and original contexts with gold answers. Table summarizes the datasets and their roles (Train or Eval) across research questions (see Section
Table Datasets and their roles across research questions. Train refers the number of samples utilized for training our CopyPasteLLMM and Eval refers to the number of samples used for eval- uation. The 20,000 samples of the PubMedQA Artificial subset were randomly sampled using the random seed 42 from the 21 kk entries.
Subset
Answer
RQ1
RQ2
RQ3
RAGTruth
QA
Daily-Life
839
Eval
only Train (16)
Eval
FaithEval
Counterfactual
Science
1,000
Eval
Train Eval (241 759)
Eval
PubMedQA
Labeled
Biomedicine
1,000
Eval
Train Eval (108 892)
Eval
PubMedQA
Artificial
Biomedicine
ConFiQA
Counterfactual & Original
Wikidata
36,000
Eval
Metrics For RQ1, we evaluate responses across multiple dimensions: contextual faithfulness using AlignScore (Zha et al. 2023) for overall answer assessment and MiniCheck (Tang et al.)
2024) for sentence-level evaluation; hallucination detection via LLM-as-Judge (Qwen3-32B rea-
soning (Owen-Team) 2025)) with pairwise comparisons (Zheng et al. (023 identify Twist and Causal hallucinations (prompts detailed in Appendix L.3 response fluency measured by perplex- ity under GPT-2; copying behavior quantified through copy coverage (x) and copy density (8); and query relevance assessed via
(Zhang et al. 2025b). For RQ2, we employ Hit Rate (following Li et al. (2025a)) and Accuracy both requiring gold answers. Hit Rate measures the extent to which methods recognize contextual knowledge presence using Chain-of of-i-uhghhhhhh (CoT)) prompting (Wei et al. 2022)), while Accuracy evaluates the degree of belief in contextual knowledge using direct answer prompting (prompts detailed in Appendix L.4). FaithEval provides ready-to-use multiple-choice options, whereas ConFiQA offers only Counterfactual and Original answers. For ConFiQA, designate Counterfactual answers correct in counterfactual contexts and Original answers as correct in original contexts. To increase task difficulty, we introduce an "unknown" option, allowing methods to express uncertainty when appropriate.
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Models & Baselines We conduct experiments using four popular open-source LLMs as base models: Mistral-7B --n--------nttuut-0.2222 (M-7B), LLama-3..--8B 1a----8--Isttcct (L-8B), Qwen2.5-72B 3-722-Instructt (Q- 72B), and DeepSeek-V3 V3-0324 (D-V3). Sopy-Paste-Promptin methods are evaluated on the four CopyPastel M is trained on M-7B, L-8B, and its predecessor -LLMA-3--8--Me models.
to
enable comparison with baselines.
Stage 1 Baselines: For Pea---astt-Promptiin evaluation, we compare against Attributed (Zhou al. 2023) and Citations-the former standard RAG approach, the latter requiring LLM- generated citations during abstractive generation (Zhang el al. 2023)). These methods serve dual ing.
purposes: validating our prompting effectiveness and providing rejected responses for DPO train-
Stage Baselines: For CopyPasteLLMM evaluation, we benchmark against state-of-the-art methods including prompting based Attributed, Fine-tuning-Be Context-DPO (Bi et al. 2025). Canoe (Si al. 2025) and ParamMute (Huang al. 2025b). and decoding- boading-based based CoCoLex (T...s.s et al.
2025
copy-based confidence decoding strategy for legal text faithfulness.
C COPY-PASTE-PROPPIHA ANALYSIS
In this section, provide a comprehensive analysis of the behavior and performance of our pro- posed Sroy-Paste-Pronedd methods (CP-Order, CP-Link, and CP-Refine). We focus on copying behavior and query Relevance.
C. COPYING DEGREE ANALYSIS
Attributed
Citations
CP-Onler
CP-Link
CP: (P-Refine
Coverage (e)
1.0
0.8
0.8
088
0.6
0,5
0.6
0.4
0.4
M-7B
L-SD Q-72B
D-V3
M-7B
Q-73B FaithEval
D-V3
M-7B
L-SB
Q-72B D-V3
RAGTruth
PubmedQA
Figure 5: Copying degree across models and datasets. Copy-Paste- Paste-Prompting methods significantly outperform baselines in and 8 (area of point). Notably, the copying degree varies by dataset nature (FaithEval > PubMedQA > RAGTruth) and model capacity, with DeepSeck-V3 balancing copying and query relevance effectively.
Figure 5 illustrates the copying degree, measured by Copy Coverage (k) and Copy Density (6), across different models and datasets. We observe three key trends:
Superiority of Copy-Paste Methods: All three Copy-Paste-Promea Prompting variants consistently achieve significantly higher copying degrees compared the Attributed and Citations baselines. This confirms that our prompting strategies successfully enforce the lexical reuse of context, which the prerequisite for our subsequent preference learning pipeline.
The ability to adhere to high-copying constraints varies by model size and intelligence. Mistral-7B 7--InBtruct--ee generally exhibits the lowest copying degree among the models evaluated. This suggests that smaller models may struggle to maintain strict lex- ical constraints while simultaneously managing coherence. DeepSeek- V3, despite being the largest and most capable model, often shows the second-lowest copying degree among ticularly in RAGTruth and PubMedQA). We hypothesize that this is due to the model's advanced capability balance conflicting objectives; rather than blindly maximizing copying at the expense of fluency or logic, DeepSeek- V3 likely optimizes for a "sweet spot" that maintains high copying while ensuring the response remains natural and logically sound.
Model-Dependee Copying Behavior:
methods (par-
Dataset-Specific Characteristics: We observe a distinct ordering in copying magnitude across datasets: Fa thEval > PubMedgA RAGTruth. (1) Fhit (Highest. Copying)
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Since this dataset focuses counterfactual robustness, the parametric knowledge is intentionally incorrect. Models are forced to rely entirely on the provided context to answer correctly. leading to maximum copying. (2) PubMedQA: The biomedical domain involves specifie terminology and factual definitions that are difficult to paraphrase without losing precision, naturally encouraging higher lexical reuse. (3) RAGT ruth (Lowest. Copying ng)) This dataset involves open-ended, real-world queries that often necessitate abstractive synthesis and summarization. Although its over- all copying degree lower than the other domain-specifff datasets, our Copy-Paste methods still effectively enforce substantial and useful lexical reuse from the context.
Attributed
CP-L.ink
CP-Order
CP-Refine
Query Relevancy
0.7
0.8
0.7
0.7
0.6
0.5
M-7B
L-8B
Q-72B
D-V3
M-7B
L-80
Q-72B
D-V3
M M:7B
L-SB
Q-728
D-V3
RAGTroth
FaithEval
PubMedQA
Figure 6: Query relevancy performance. CP-Refine consistently yields the most relevant responses. The efficacy of CP-Link is model-dependent; only the highly capable DeepSeek-Ve V3 utilizes the linking mechanism to improve relevance the rigid CP-Order approach.
C.2 QUERY RELEVANCY ANALYSIS
As shown in Figure CP-Refine consistently achieves the highest query relevancy among the three proposed methods. This validates the effectiveness of the "Reviewer" component in our soft- constraint loop, which explicitly critiques and guides the "Writer" to address the query while main- taining copying. The performance CP-Link strongly correlated with model intelligence. While CP-Link is designed to improve upon CP-Order by adding transitional text, only the most capable model, DeepSeek-o V33 successfully leverages this freedom to enhance query relevance over CP- Order. Smaller models (e.g., Mistral-7B) often fail to generate meaningful transitions, resulting in performance similar to or lower than the strict CP-Order method.
D IMPLEMENTATION DETAILS
D.1 COPYPPASTE-PROMPIII
Algorithm Copy-Paste- Constructing High-Copying Responses
Require: Query Context C. Method M (CP-Order, CP-Link, CP-Refine), Threshold 0., Max iterations Tiaa Ensure: High-copying response A
1: if (CP-Order, CP-Link]
cand-constraint
2:
{$]
] <- ExtracReedeamSSeeeeneeCCCCC Q)
b Common extraction
3: M = CP-Order then
> Direct sentence ordering
4:
A (irectOnderinn((::))..0
5:
ekse
CP-I Ordering via transition generation
6:
A Generate Transstion Withorrrinn ((+.).
7:
end if
8:
else
CP PePRefine: Soft-comstraint
9:
(100_ - Writer(Q. C-
10:
while e Timux (!) < do
Uatil copying meets threshold
11:
foodback +- (1v) Q)
D Verbal supervision on relevance & fluency
12:
((t)) m()(()) [)) ()n ex,))
Copy
13:
i( 2 0, then
Hard constraint copying
break
15:
end if
16:
A (c+1) - Writer(Q, C, feedbbck). + (
17:
while
18:
A At
end if
20:
return A
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The Paste-Prompting methods were implemented using modular pipeline architecture. For the CF-Order method, we employed a text similarity threshold of 0.95 for validating extracted sentences against the souree context, utilizing both direct string matching and fuzzy matching with sliding windows to ensure strict extraction accuracy. The CP-Lime nk method generates transition sentences with constraint of more than 15 words to maintain conciseness while ensuring logical flow. For the CP-Reff Rine, we utilized an iterative refinement process with a dual-agent system (Writer and Reviewer) implemented via LangGrapt This process was configured with maximum of T max 5 iterations and target copying score threshold 0, 0.99. The composite copying score (1) is calculated using hyperparameters. a = 0.6, 8 0.25, = 4, and = 0.4, effectively balancing the contribution of copy coverage (N) and copy density (6) guide the generation toward high contextual fidelity. All prompting methods were run with a low temperature setting (0.1) to ensure consistent outputs.
D.2 COPYPASTELLM
Algorithm 2 CopyPasteLLLM: Automated Preference Construction and Training
Require:
1: Query-conteat pairs [[Q,, CO))
2: Methods T (Base, Altributed, Claations, CP-Order, CP-Link, CP (P-Refime)::
3: Metrics (fz. 03)9-11 5-1 Temperature
Ensure: Trained model mh with internalized contextual belief
4: Initialize D +-
5: for each (Q., Ct) do
6: R: <- [GenenateResuonee( Qu Ca. C))m) :
Generate candidates
7: R² + fr R. A3-1 (r) p4, 8,)}
MMult-criteria filtering
8:
ratings EboTouramen(RR CC))
Pairwise L.I as Judge with Elo scoring Select hest response
9: ri arg max,
maings(r] FERR meR! m
10:
if Afall and ANraR available then
D Handle samples with answer annotations Append gold answer transform reasoning into conclusion Rejeent fr Arme : R/ (CP-Order, CP-Link, CP-Reline) \ {r?}}
11:
oehien +- rf @ AP
12:
b. Append wrong answers to
methods
13:
D DU ((Q. 60 C. thoon ; E R (R( \ {CP methods])]
14:
Handle samples without answer annotations
15:
((Q. C
R (rt))
Use original responses without answer appending
16: end
17: end for
18:
Initialize f). Fiet
DPO training with 5.N preference pairs from samples
19:
while converged
20:
for cach (it, Mwn ) E D do
bs Leverage 5x data officiency: each sample yiolds 5 preference pains
21:
L log
3 log [a(2ll)
"ailMi Boo lal (
22:
Update 0 using V
23: end for
24:
end while
25:
return wig
We fine-tune CopyPasteLLLM on three instruction-tuned b 2
LLaMA-3 1----B-nnee ruct and Llama 1.mma-3... Optimization (DPO) with parameter-efficient LoRA adapters, based on responses generated by DeepSeek-V3.nn V3-0324. We adapt attention and MLP projections (a-proj. k-proj, v-proj, o-proj, gate-proj, up-proj, down_pro with r = 64, = 128, and dropout=0. Training uses a maximum prompt length of 8192 and maximum generation length of 1024: the per-device batch size is 2, combined with 8 aaadient-accumulltion steps. We optimize with Adam (learning rate Se-5, weight decay 0.01, max gradient norm 1.0) under a cosine schedule with 5% warmup and no label smoothing: the DPO temperature is set to 3 0.3. To balance compute and convergence, we train for 2 epochs on Mistral-7B-Insttruttvv.. and LLaMA-3 1-MM----BB-- ----------t--ttt....----------- and for 1 epoch on 1--ma--3.--8-IIntttt
3. 1 1--8---- Instruct lusing Direct Preference
'htts://github.com/// htgp:///w..oommm/ggccccaa/aggggg
"htrss//huggingface.coo wwtpeeeeo//eeeeeeeeeeeeeeeeeeeee com/metaallamm//Meta --aa--------neeee
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E TASK-SPECIFIC PERFORMANCE EVALUATION OF COPYPASTELLM
To gain a deeper understanding of CopyPasteLLMMss robustness and versatility, we conduct a fine- grained performance analysis acToss three dimensions: conflict complexity (ConFiQA), knowledge domains (FaithEval), and reasoning ambiguity (PubMedQA).
E.1 IMPACT OF CONFLICT COMPLEXITY AND MULTI-HOP REASONING
We utilize the three subsets of the ConFiQA (Bi et al. 2025) dataset evaluate how CopyPastelLLLM handles increasing levels of reasoning complexity and counterfactual conflict density:
ConFiQA-QA (Question-Answering) Represents single-hop reasoning with single point
knowledge conflict.
Con (Multi-hop Reasoning):
Involves multi-hop structures where only one step contains knowledge conflict, testing the model's ability to integrate counterfactuals into reasoning chain.
ConFiQA-MC (Multi-Conflitss: The most challenging setting, featuring multi-hop struc- tures where all reasoning steps are modified to be counterfactual, creating a global knowl- edge conflict.
As shown in Table I CopyPasteLLM demonstrates exceptional robustness as task difficulty in- creases. On the ConFiQA-MC subset, where models must adhere to a completely counterfactual reality, baseline performance typically collapses. For instance, on Ilama-3.1 1-8B, the Attributed method achieves only 15.5% accuracy. CopyPasteLLMM maintains remarkable accuracy of 83.5%, strictly follows the instruction of generating responses based on context, and significantly mitigates internal parametric resistance. Notably, the Mistral-7B backbone, CopyPasteLLM (trained on only 365 samples) achieves 82.5% % on the MC subset, outperforming even the Context-DPO method (80.4%) which was trained 18,000 samples seen during training.
E.2 PERFORMANCE ACROSS DIVERSE KNOWLEDGE TYPES
Attributed
CoColex
Caroe
Parammute
Context DPO
CopyRasteLLM
102%
Accuracy
83%
43%
Basii Facts
Processes
Experimentstegg 951
Teleo Purposel
De
Structure(
1igebraict
Spatia
Properties
Figure 7: Performance comparison across diverse knowledge domains. CopyPastel.LMM consis- tently outperforms or remains highly competitive against strong baselines across most categories, demonstrating robustness in both factual (e.g., Basic Facts) and reasoning-intensive domains (c.g., Processes & Causal, Experiments).
To analyze performance across different knowledge domains, we classify the FaithEval- Counterfactual (Ming et al.] 2025) dataset samples into eight distinct knowledge types (e.g.. Ba- sic Facts, Processes, Experiments). Since the original dataset lacks these labels, we employed a strong model, DeepSeck-V3. V3.1. to classify the samples based on the taxonomy defined in the ARC- Challenge (Clark et al. 2018). We utilized a Chain-of -hoohhh (CoT) prompting strategy with majority voting (temperature sampling over 5 runs) to ensure classification reliability. Figure 7 il- lustrates the accuracy breakdown. CopyPasteLLMM (green bar) consistently outperforms or matches the strongest baselines across all categories.
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E.3
ROBUSTNESS TO REASONING AMBIGUITY
Llama-3-8Blnnsteee
Llama-3.11888 --8--nnrttt
Attrloned
CopyPasie
Atriliated
CopyPasiel 2
90%
80%
80%
Accuncy
Accuncy
70%
70%
60%
60%
50%
50%
40%
40%
Overall
Consensus
Nogatiation
Overall
Consensus
Negotiation
Figure 8: Performance breakdown on PubMedQA-Labeled by reasoning difficulty. Accuracy is compared between Attributed and CopyPasteLLLM models across the Consensus (clear evidence) and Negotiation (ambiguous context) subsets, demonstrating the highest gains in samples with explicit evidence.
We further analyze performance on the PubMedQA-Labeee (Jin et 2019) dataset, distinguishing between samples based on reasoning setting as defined by the original dataset annotators:
Consensus: Samples where independent experts agreed on the answer easily, implying the context provided clear, unambiguous evidence.
Negotiation: Samples where experts initially disagreed and had negotiate final answer, implying the context was ambiguous, implicit, or difficult to interpret.
Figure presents the accuracy on these splits. We observe a distinct pattern:
Clear Evidence (Consensus): LM delivers significant improvements. For Llama-3-8B, accuracy improves from 75.49% (Attributed) to 86.85% (CopyPasteLLLM)); for 1.ama-3.1-8B. improves from 76.79% to 84.58%. This confirms that when explicit evidence exists, method effectively anchors the model to it.
Ambiguous Evidence (Negotiation): The performance gains more modest neutral. For Llama-3-8B, the performance has slightly improved (48.91% vs 51.27%), while for Llama-3.1-88. its performance is slightly lower than the baseline (46.38% vs 44.93% This result is expected and rational: the Copy-Paste pattren relies on the existence of text fragments that directly support the answer. In "Negotiation" samples, where the evidence is implicit or ambiguous, there may be no clear fragments to copy that definitively resolves the query.
Figures and 10 compare the accuracy of the Attributed and CopyPasteIII across the top frequent MeSH terms, demonstrating consistent performance gains in handling specific medical topics by enforeing contextual adherence.
F RESPONSE LENGTH ANALYSIS
To further investigate the behavioral characteristics CopyPasteLLM under the Chain-of-Thoughh (CoT) setting, conduct granular analysis of response length, copying degree, and the semantic relevance of copied content. The statistical comparison against five baselines (based on LLaMA-3- 8B-Instrrue) is presented in Table
Response Length and Reasoning Capability. As shown in Table 5 there is a significant diver- gence in response lengths among methods. ParamMute (Huang et al. 2025b) exhibits an extremely short median length of 4.0 tokens. This indicates failure adhere to the CoT instructions: instead of generating reasoning chain, tends to output the final answer option directly, potentially ex- plaining its suboptimal performance in complex reasoning tasks. Conversely, abstractive baselines
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100%
Attibatted CopyPusteLLMM
Accuracy
60%
70%
82%
200 coatee
the monn Gaaee
(O0) Car und DA Phoee
Cnteen unmiiraa doay pieur op
with salairad
wow foon
Figure 9: Nomain--pecitfe performance analysis of CopyPasteLLMM (based on Llama-3 ---aa------i-n 3-8b-instruct) on PubMedQA, categorized by Medical Subject Headings (MeSH).
100%
Attributed
CopyPasteLLM
90%
Accursey
80%
you
to
of aottin 1921) Ceaann Soaa or
the coe
Coyar you
Figure 10: Domain-speciffe performance analysis of CopyPasteLLMM (based on Llama-3.1-8 instruct) on PubMedQA, categorized by Medical Subject Headings (MeSH).
Table 5: Response Analysis by Method under CoT setting (Base: LLaMM--38BB-nstmee) Metrics reported are Median + Standard Deviation.
Method
Length
K (Coverage)
5 (Density)
Attributed Zhou et al.
2023 2025
199.0 1 68.29
0.552 1 0.129
2.70 2.85
CoCoLex (Lyss et al.
75.0 40.01
0.989 = 0.040
50.08 + 37.68
Canoe Si ct al. 2025
198.0 = 69.30
0.551 = 0.126
2.67 + 3.68
CP (Bi et al.
2025)
159.5 = 62.72
0.631 = 0.130
4.17 + 4.32
ParamMute (Huang el al.
20255b
4.0 + 18.69
1.000 + 0.182
3.00 + 13.23
CopyPasteLIIM (Ours)
126.0 + 78.71
0.844 + 0.135
10.49 + 15.49
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like Artributed and Canoe generate longer responses (<e 198 tokens) but with lower copy density (Ō 2.7), suggesting a reliance on internal parametric knowledge for generation. LopyPasteLLM maintains moderate and sufficient response length (126.0 tokens), striking balance that allows for adequate reasoning steps while enforcing grounding through copying.
Attributed
CoCoLex
Canse
ParamMute
Context-DPO
CapyPasteLLM
Longth Copyled Fragments:
Query Relevancy
0.4
0.2
24
Length Copyled Fragments:
Relevancy
0.6
0.4
Longth Copyled Fragments: 136
Relevanty
0.6
0.4
0.0
105 112
Figure 11: Query Relevancy analysis of copied fragments across different length buckets. The height of the bars represents the average cosine similarity between the copied fragments and the query. CapyPasteLLMM (green) maintains competitive relevance across all lengths.
Rational vs. Blind Copying, Analyzing the copying metrics reveals distinct strategies. Co- CoLex (Ty.s.s al. 2025) achieves near-perfect copy coverage (x 0.989) and an exceptionally high copy density (6 ~ 50.08). However, combined with its low Hit Rate of 17.9% on FaithEval (refer [ this suggests tendency towards "blind copying"-reproducciii"
the entire context verbatim without selective filtering or logical reasoning. In contrast, CopyPusteL..MM demonstrates a high but rational copying behavior (w 0.844, 10.49). copies significantly more continu- ous spans than standard baselines (Context-DPO's ò z 4.17) to ensure faithfulness, yet avoids the unselective copying observed in CoCoLex
Query Relevancy of Copied Fragments. To verify that CopyPasteLLMM copies meaningful evi- dence rather than irrelevant noise, we analyze the semantic relevance of copied fragments in Fig- IT. We extracted all copied fragments with length 2 2 using Algorithm Saritthmmaan and calculated their cosine similarity with the input query using text embedding model. The results indicate that across various fragment lengths (ranging from short phrases to long sentences). CopyPasteLLMM maintains consistent and high level of query relevancy (comparable that our method effectively identifies and copies context segments that are semantically pertinent to the user's question.
exceeding baselines). This confirms
G ABLATION STUDY AND TRAINING DYNAMICS ANALYSIS
To rigorously dissect the contribution of each component in CopyPasteLLMM and evaluate its training stability, conducted ablation studies based on the Llama-3.1-8B-Innee terfactual subsets of ConFiQA (QA, MR, and MC) as the testset. All models were trained on the same 365 preference pairs for epochs (218 steps). To ensure statistical significance, evaluated
We utilized the coun-
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a) ConFiQA-QA
90%
CopyfasteLLM (Accuracy after CoT)
CopyPasteLLM (Hit)
Score (CI 95%)
80%
w/o Copying (Accuracy after CaT)
wio Copying (Hit)
Stamping (Accuracy
Stamping
Base (Accuracy after CaT)
Base (Hit)
70%
60%
50%
40%
10
30
50
70
90
110
130
150
170
190
210
b) ConFiQA-MR
80.0%
70.0%
Score
60.0%
50.0%
30
50
70
90
110
130
150
170
190
210
c) ConFiQA-MC
90%
80%
Score (CI 95%)
70%
60%
50%
40%
30%
10
30
50
70
90
110
130
150
170
190
210
Step
Figure 12: Ablation study and training dynamics on ConFiQA datasets. Solid lines represent Accuracy after CoT (strict JSON format, see Prompt [..6), and dashed lines represent Hit Rate (see Prompt L.4p Shaded areas indicate 95% confidence intervals across 8 random seeds. Copy- PasteLLM (Green) consistently outperforms variants without high-copying data (Grey) and without answer stamping (Purple).
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the models every 10 steps using 8 different random seeds (0 (0-6, 6, 42) with temperature of 0.7 and top-p 0.95. The results, visualized with 95% confidence intervals (calculated via t-distribution), presented in Figure 12
G.1 IMPACT OF HIGII-COPYING PREFERENCE DATA (w/o COPYING)
To assess the necessity of our specifie high-copying response construction, we implemented the w/o Copying variant (grey lines). In this setting, excluded the three Copy-Paste Prommtin methods (CP-Order, CP-Link, CP-Refine) and constructed preference pairs solely from standard baselines (Base, Attributed, Citations), selecting the top 365 samples based on multi-criteria filtering (see Figure 2
As shown in Figure 12 the full LopyPasteLLM (green lines) consistently outperforms the w/o Copying variant across all three datasets, particularly in the more challenging Accuracy after CoT metric (solid lines). Even when standard RAG responses are filtered for high faithfulness, they lack the explicit lexical anchoring provided by our Copy-Paste. The performance gap indicates that the specific structural characteristic of CopyPaste-not, just semantic correctness-iis stronger supervision signal for suppressing parametric hallucinations (refer to Appendix [A] for mechanistic interpretation of Copy-Paste). The high-copying preference data effectively teaches the model to prioritize the retrieved context over internal knowledge.
G.2 SIGNIFICANCE OF ANSWER STAMPING (w/o STAMPING)
We evaluated the role of Stamping Answers step (Stage 2, Step 3 in Figure 2 by removing it, denoted w/o Stamping (purple lines). In this variant, the model learns from the chosen response's reasoning trace without the explicit appending of the ground-trute answer label.
The results reveal critical insight: removing stamping leads to drastic performance drop, often falling close to Base model (black horizontal line), especially on the Accuracy metric. The Accuracy after CoT metric requires the model output strict JSON format { ("reascning":
" "answer": "....) "}. Without stamping, the DPO optimization primarily aligns the reasoning style but fails to enforce definitive commitment the correct conclusion. The huge gap between the solid purple line (Accuracy) and dashed purple line (Hit) in ConFiQA-QA (Figure 12p) suggests that while the model might mention the correct entity (Hit). struggles to formalize the final answer without the explicit "conclusion-forcin" signal provided by stamping.
G.3 TRAINING DATA EFFICIENCY AND STABILITY
Figure 12 also illustrates the training dynamics over 218 steps.
Rapid Convergence and Data Efficiency: CopyPasteLLM exhibits remarkable learning effi- ciency. The performance curves generally rise sharply and reach plateau around step 120-130 (approximately 1 epoch). Notably, on the ConFiQA-MR and ConFiQA-MC subsets (Figures 12 b/e). the model approaches its peak performance that the high-copying preference signal provided by our method is highly potent, enabling the model to realign its internal belief mechanism with minimal data updates. The marginal utility of increas- ing data volume appears to diminish quickly, indicating that performance gains are driven primarily by the quality and precision of the constructed preference pairs rather than the sheer scale of the dataset.
early as step 50. This rapid saturation suggests
The narrow shaded areas (95% confidence intervals) across 8 random seeds indicate that our method is highly stable and reproducible. Unlike the w/o Stamping variant, which shows higher variance and instability (wider purple bands in Figure [2p/c), CopyPasteLLMM consistently converges to a high-performance state regardless of generation randomness.
Robustness:
H PERFORMANCE OF MAINSTREAM MODELS ON FAITHEVAL
The FaithEval counterfactual subset presents challenging benchmark where mainstream LLMs demonstrate surprisingly low performance, with more powerful models often achieving lower
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euracy rates (see Table 6). This counterintuitive pattern suggests that larger models may rely more heavily on their parametric knowledge, leading reduced contextual faithfulness when faced with counterfactual information.
Table 6: Performance comparison on EaithEval counterfactual subset. The table reports accuracy scores of mainstream models from the EvithEval (Ming al. 2025 alongside our Copy method evaluated on three 7-8B parameter models. Bold values indicate our best performing method, Underlined values indicate the second-bess performing method and Italic values indicate the third-best performing method.
Model
Accuracy (%
Mistral-7B-Instruct--
73.8
Alama-3.1-8B3 -nSsttrruuct
68.5
Llama-3-BB-Instru
66.5
58.3
gpt-3.5-turbo
57.1
Command R
69.3
Phi-3.5-mini-nnstrus
66.8
Command R+
73.6
gemma-2-9b-it
55.7
gemma-2-27b-it
55.7
gpt-4o-mini
50,9
Thi-3-mini-122k-inntte
75.7
Phi-3-medium- 100--umm----2----tureee
60.8
Llama-3.1-70B Instructt
55.2
60.5
Claude 3.5 Sonnet
73,9
gpt-4-turho
41,2
gpt-4o
47.5
CopyPasteL.IN (Based on Llama-3-8B-Instrice
92.8
CopyPasteLLLM (Based Mistral-7B-Inssiee (1B-Insttruct-0022)
89.3
CopyPasteLLLM (Based Llama-3.1-80 1-B--...-8--nttrree
92.6
I COPY FRAGMENT DETECTION
The following copy fragment detection algorithm is adapted from Grusky et al. 2018) and in- cluded here for completeness of this paper.
Algorithm 3 Copy Fragment Detection
Require: Context sequence Jep, Ca....r Cm 1l: Answer sequence [4o, d1, ... m1-
Ensure: Set of copy fragments F J2.........
1: i
2: while
3: fmas 4- 0, M + (ile m 1).0, = az)
b. Find all matching positions in context
4: for m E do
5:
6: while f < and m + < m and a:44 Cmtt
7:
8:
end while
9-
Laax then Emax
10:
end
12:
end for
13:
Emax
>> 0 then
14:
F U (ass, autt. ... Aemos 1]}
Copy matching subsequnces fragment
15:
16:
ebse
17:
18:
end
19:
end while
20:
return F
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J ANALYSIS OF CONTEXT-PARAMENT COPYING CAPTURING
Algorithm 4 Context-Parametee Copying Capturing
Require:
generales loken answer Ans length n. P;: logits distribution of Hf: hidden states of the i-th token, V: vocabulary of L.M. Agux' token answer generated without context, K; scope of knowledge capture.
Given string cunlext query,
Ensure: Captured knowledge logits and hidden states Pax. Pan, Has, Hym
1: Initialize Pm, Ples. Har. Hpo 0. Tax, Tjon +-
Token lists for captured tokens
2: Sam commonSubsttingMaachigg (C, Apux)
Identify common substrings
3: for [1,
do
4: " softmax (P.)
Normalize logits to probubility distribution
5:
V. son(V, P()
D. Sort vocabalary tokens Pi descending order i Only consider the top-K most likely tokens
6:
for in 2...., K]
7:
2, V.Dl
Get f-th most probable token Skip meaningless tokens, function words
8:
if iisMeaningloss(() then continue
9:
end if s in Son then break
10:
D- iy is common both context and parametric generation
11:
end if in C. and H & Tax then
12:
Capture contextual knowledge token
13:
Par +- Pax (P: (P.,,). Hga Ha U (H:), Tea +- Ta (nn)
break
14:
end if
15:
if v in Ape and it $ Tau then
p. Capture parametric knowledge token
16:
Pas + Pren U (P Hpro + Haaa U (Hi), Ti Tau + Tono (as)
break
17:
end If end for
18:
19:
end for
20:
return Pass Paess Hex:
This section provides comprehensive analysis of our Context-Parameter Copying Capturing algo- rithm across multiple datasets and model architectures. Figure 13 presents the complete logits power distribution analysis across all three datasets (RAGTruth, FaithEval, PubMedQA), reveal- ing how Copy PMpteeLLL and base models differ in their reliance contextual versus parametric and
knowledge throughout the generation process. Figures and TnndT5pp [5] the main text analysis by showing hidden states distributions FaithEval and RAGTruth datasets, demonstrating the mantic separation between contextual and parametric knowledge representations in CopyPasteLLM compared to base models.
Logits Power Caleulation Formula We employ the following formula to calculate the logits power for each response token, measuring the model's reliance on contextual versus parametric knowledge during generation:
logits_power
X n
where & denotes the logit value of the i-th token, and represents the number of samples in the dataset that have contextual or parametric knowledge at this position.
K LIMITATIONS AND FUTURE WORKS
While CopyPasteL.LM demonstrates remarkable effectiveness in enhancing contextual faithfulness through high-copying behavior and achieves substantial performance improvements with excep- tional data efficiency, several promising directions warrant future investigation.
Our current framework assumes that the provided context contains sufficient information to answer the query. When context incomplete or lacks relevant details, the copy-paste paradigm may struggle to generate satisfactory responses. Future work could explore adaptive mechanisms that dynamically assess context sufficiency and gracefully handle information gaps, potentially by incorporating uncertainty quantification developing hybrid strategies that selectively combine contextual and parametric knowledge based on context completeness.
Incomplete Context Scenarios:
Context-Parameter Copying Capturing algorithm provides valuable insights into logits and hidden state distributions, a more comprehensive mech- anistic analysis could examine the roles of specific model components such as attention heads and
Deeper Mechanistic Understanding: While
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Copy-Paste
Base CTX
Copy-P
Base
Mistral-78-instruut--oo
Llama-3, 1rmma-3.--8-Innttree 8888--nstttut
839/839 (100.0%) Samples
839/839 (100.0%) Samples
Logits Power
500
2000
RAGTruch
1000
149
298
447
596
745
98
196
294
392
490
1000/1000 (100.0%) Samples
1000/1000 (100.0%) Samples
3000
Logts Power
2000
Evall
1000
1000
1000
420
66
132
198
1000/1000 Samples
Samples
3000
Logits Power
PubMedQA
1000
229
342
456
01
162
324
405
Response Length
Response Length
Figure 13: Logits power distribution across response lengths for contextual (CTX) and parametric (Para.) knowledge. Values above x=0 indicate CTX logits power, values below x=0 indicate Para. logits power (negated for visualization).
Base CTX
Base Para.
Cosy: Paste
Copy:Paste Para.
Mitrral77 -----B----- (B-istruct-v022
Utamn-33 3.1 08 instrcc
Copy-Pasta
CTX
Base Paraa vs Cosy-Paste
Figure 14: Dimensionality reduction visualization of hidden states distributions between contextual (CTX) and parametric (Para.) knowledge on FaithEval dataset across two base models. Each subplot shows pairwise comparisons with marginal KDE distributions and confidence ellipses.
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Copy
Copy-Passa Para.
Mistral- TB- TB-Inntrrcttvo..
Liama 3, -aa- an--neerett
Base Base
Copy-Paste CTX vs Copy-Paste Para.
Copy-Paste CTX
Baise Para, Capy-Paste
Figure 15: Dimensionality reduction visualization of hidden states distributions between contextual (CTX) and parametric (Para.) knowledge on RAGTruth dataset across two base models. Each subplot shows pairwise comparisons with marginal KDE distributions and confidence ellipses.
feed-forward networks (FFNs). Understanding how CopyPastel.IMM affects attention patterns across layers and how FFNs process contextual versus parametric information could reveal finer-grained mechanisms underlying our approach's 's effectiveness and potentially inform more targeted architec- tural modifications.
Multimodal Contextual Faithfulness: An intriguing extension involves applying the copy-pase paradigm to multimodal scenarios, particularly in domains like medical imaging where models might favor parametric knowledge over visual evidence. For instance, when interpreting medical im- ages, models may overlook subtle but critical visual details (such as minor variations in ECG wave- forms radiological abnormalities) in favor of common parametric patterns. Investigating whether copy-pass pasye principles can be adapted to enforce stronger reliance on visual context- context-perhaps through visual attention mechanisms or multimodal copying strategies-represse represents a compelling avenue for enhancing faithfulness in vision-languaa tasks.
L PROMPTS
Here the prompts in experiments.
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L.1
PEAPAATTE-PRMPPIN METHODS
L.1.1 RELATED SENTENCE EXTRACTION
Related Sentence Extraction
Instruction: Pleuse carefully read Contest extrett ALL relevant complete seniences could help answer the Query. Output each extracted
sentenez on seporat line, proceded by "EXTRACTED:"
Coarext
{comtesr)
Query
{awery)
CRITICAL REQUIREMENTS
You MUST
complete
they appear Contest.
2. NO modilications, punphhmsing. or cumbining of sentences allowed.
3. Each extracted seiience must be highly relevant to the Query.
4. Extract AL sentenees that could help anewer the Query (or on the side of inclusion).
arminology, measurmmnnts symbols exactly
Ouipun Fonsat
EXTRACTED: [Finst complete sentence exartly as it appears in Comexi]
EXTRACTED: [Secomd complete exaclly appears Context]
Your extraction:
L.1.2 CP-ORDER
CP-Order
Instraction: Given the Query and list of Copied Sentencos, please deternine the optimal onder for these sentences create the most logical, coherent, helpful response:
Query
[querr)
Copied Santences
(sumbered semnencer)
Important Ropirements
Dally use the sentence IDs grovided above
Inellde ALL sentences your ordering
Consider query
when detemining
logical
Cutput
Output the optimal order as comma- comma-sqaanted list sontence IUs as below, not provide any other information.
ORDER: [oomina-se sepeeepppttttte sted list sentence IDs SENT 2,SENT 1,FENT
L.1.3 CP-LINK
CP-Link
Instraction: You are a professional text organization expert. Generste concise tansilion sentences to connect the core sentences and make the response flow maturally.
(qwery)
Senteness fuunbbrre_eenttnecs
Requirements
Transition sentences should concise tham womhs) adjacett Focus on creating smooth flow between ideus
should logically
4. Common types: progression, contrasi, addiium, conclusion
Ohrput Fonnal
(TRANSITION_] 221traniition contes//TRANSITTOOOO
(TRANSITION_2
2.30ON2_33rrrmiisss 3tramsition senience SIMMlem//RANNSTTIION
Optionally add:
[INTROjintssuccion senimnes/INTRO]
[CONCLUSION SION sclesse/CONNLUUSIN
Pleuse generate transitions:
L.1.4 CP-REFINE
Copying Requirements
RELEVANT REUSE: Incoeporate essential connections only.
3. PRESERVE EXACT WORDING: Keep original phrases and exprassioss
4. CONTEXTIONLYY INFORMATION: Use only Gets explictily in the context, de make information.
ENGLISH
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Writer w/o Reviewer's Suggestions
Instraction: You are wriler, skilled cupying relevant content from comiest to answer user questions, Generate highly cupying responses from the given
Query
(query)
Context
(contor) }
Copying Requirements
Answer:
Writer w/ Reviewer's Suggestions
Instruction:
You are Writer, skilled at copying releverr content from contest answer user questions The Reviewer has suggested revisions your old Please provide a better improve and query relevanue.
Revieser's
Old Answer
(old anlwere }
Reviewer's 's Suggestions
freveeerr ]
Context
(contest)
Query
(avery)
Copying Requiremens
Answer:
Reviewer
query suggest goul improving
copying
query relevance.
Context
(contor)
Query
(query)
Answer Awaiting Review
(onwer):
Copying Score: Text reuse from vontext (Curreni: (copying_rcom)
If copying wome (copying Jhreshold', ), require more contest incorporation
Comestual Fathfulnees:: facts sourced context
knowledge Reduce excessive unnceessary original content
Query Relevance: Direet addressing of user query
Provide CONCISE and ACTIONABLE suggestions (mux 3 points);
L.2 BASELINES PROMPT-BASED
L.2.1 BASE
Base
(mery)
L.2.2 ATTRIBUTED
Attributed
Instraction: Bear mind that your unswer should strictly bused the following contest.
Contest: (contesz)
Query: {query)
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L.2.3 CITATIONS
Citations
Instraction: Bere in mind that your anvwer shoul srietly based on the following mimbered passagos. Add cinnions square tenckets [1], 32. cte. the end of sentences diat are supported the evidence.
Numbered
[uumberrdd sentences)
Query
(murry)
L.3 PROMPTS OF LLM JUDGES
We design the pairwise-comee comparison template and instructions to enable systematic, fine- tne-grained evaluation of hallucinations in RAG responses.
Pairwise Comparison Template
Instraction: You expert judgeo Compare RAG responses (Rosponse A and Response R) (destraction)
Contest: (contesz)
Respomse [responne.r)
Response R: [mrgonss_)) B)
Please note: Do not question or doube the provided contest. Assame the comeer abvolunely correct, and make verdier stricrly based on this premise.
Ourpui {4 ""verdie"" "<A//TIE>"))
Above template is method -aghtssi:: it presents two anonymous responses, a common context treated ground truth, and requires judges output formatted verdict A, B Tie.
The three instructions below can be slotted into the {instruction) placeholder in the above template and each then serves pick the response exhibiting fewer RAG hallucinations along its respective dimension. Fabrication focuses on statements that are wholly unanchored in the provided context. Information-Distoriie focuses statements that misalign with the explicitly given context. False- Association focuses on claims that misweave separate pieces of context into an unsupported whole.
Instruction for Comparing Twist Hallucination
information distertica ballucination. The Core Definitinn Information Twist Altering key information the Context subjects, conclusions).
Which has fewer information distortion Iollucinaiinss
Instruction for Comparing Causal Hallucination
causal hallucination The Core Definitonn of Causal: Foruibly linking unrelated content the Context form comclusions unsupported the
Which has fewer false associaioo hallacination??
L.4
HIT RATE
Hit Rate
Context: (comient) }
Question: (question) }
Basod the context, let's think step-by-step and neswer the questiion detail. Answer:
L.5 ACCURACY
Accuracy
Context: {contear)
Question: [guestion)
Options: (eptions)
Based on the above context, answer the question. You must outpur only single token: A. B C. or D. Do not prwide any explanation or reasoning, just the chosen option. Answer.
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L.6
ACCURACY AFTER CoT
Accuracy After CoT
Conrext:
[contesr
Question: [question)
Options: [sptiions
contest, stop stop-by--peeee atep Give your rcesokning process dirst, then peovade option.
Ouipui format should ISON object with two fiekss: "reasoning" and "unswer" sach as: "reasoning". "unswer"
Answer:
M USE OF LLMS
We used large language models solely for proofreading purposes to check spelling and grammatical errors in this paper.
33


Reviews:
[{"review_id": "vA5XVT1o9P", "reviewer": "Reviewer_jspG", "summary": "This paper presents CopyPasteLLM, a two-stage framework designed to mitigate hallucinations in RAG systems by promoting high-copying responses. The authors hypothesize that directly copying context fragments, rather than reinterpreting them, improves contextual faithfulness and reduces hallucinations. The framework employs CopyPaste-Prompting methods to generate high-copying responses and utilizes DPO to internalize these preferences into the model. The proposed method is shown to achieve significant improvements in both counterfactual and original contexts, with data efficiency being a notable strength, requiring only 365 training samples compared to existing methods that need much more data. The authors also introduce Context-Parameter Copying Capturing, an interpretability tool that tracks model reliance on contextual vs. parametric knowledge.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The CopyPaste framework introduces a novel approach to addressing the long-standing issue of contextual faithfulness in RAG systems. By focusing on maximizing lexical reuse and reducing re-interpretation, the method offers a clear and intuitive solution to mitigate hallucinations.\n\n2. Despite only using 365 high-copying samples for training, it outperforms the best baseline models that require significantly larger datasets (e.g., 18000 samples). This makes the method potentially very practical for real-world applications with limited data.\n\n3. The authors provide extensive experimental evaluations on multiple datasets (FaithEval, PubMedQA) and across various models, demonstrating the robustness of CopyPasteLLM. The method achieves consistent performance improvements, particularly in challenging counterfactual scenarios, and significantly reduces hallucination rates.", "weaknesses": "1. The proposed copy-paste strategy directly copies context fragments into the generated response rather than reinterpreting them. While this improves contextual faithfulness, there is a potential downside in terms of response length and noise. For instance, copying large portions of text verbatim may lead to excessively long responses or include irrelevant content, ultimately degrading the quality of the output. Additionally, generating high-copying responses could introduce significant computational overhead, particularly when dealing with longer or more complex contexts. I suggest that the authors discuss this potential issue in more detail and consider possible solutions to mitigate any associated computational costs.\n\n2. I believe the copy-paste strategy may be sensitive to the complexity and type of knowledge involved. The method could perform differently when dealing with simple factual information compared to complex or abstract knowledge, where strict copying might not be as effective. For example, copying highly specialized content might not always improve the quality of the response if the copied content is not sufficiently detailed or applicable to the query. The authors should include ablative experiments to analyze how the method performs with different types of knowledge (e.g., simple factual knowledge, abstract reasoning, or specialized technical knowledge) and how it behaves across tasks of varying complexity.\n\n3. The paper suggests that different extraction strategies (strictly extractive and softly refined) play an important role in improving contextual faithfulness. However, the paper does not provide enough detailed analysis of how these methods differ in performance. For example, CP-Refine adopts a soft constraint and involves an iterative refinement process, which might introduce different performance characteristics in terms of fluency and contextual accuracy. I recommend the authors conduct additional experiments to analyze the impact of these extraction methods more thoroughly, including the effect of the initialization approach and other experimental details on the results.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "twldalpXCK", "reviewer": "Reviewer_ujdf", "summary": "This paper proposes CopyPasteLLM, a two stage framework to reduce context unfaithful hallucination in RAG. In stage 1 they create high copying answers via three prompting models, CP-Order, CP-link, CP-Refine, while in the stage 2 they convert these candidates plus standard baselines into preference data using multi criteria filtering.\nCopying degree is quantified with Copy Coverage and Copy Density, adapted from Newsroom. The paper also introduces Context Parameter Copying Capturing, a token-level probe that compares with-context vs no-context decoding to attribute reliance to contextual versus parametric knowledge through top-k logits along chain of thought. Experiments on various datasets report large gains with only 365 samples for DPO.", "questions": "1) Table 3 states PubMedQA is evaluated on 20k samples, whereas in Table 4 it is mentioned size of PubMedQA as 1000, this needs some clarification.\n2) Authors should introduce some sort of confidence intervals to their results reported, as the current results are point wise, and CI should strengthen their claims, also across multiple seed values.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) The author clearly operationalize “copying” as a proxy for faithfulness by using κ, δ metrics which are defined cleanly and tied to an algorithmic fragment detector, the paper keeps the goal explicit: increase lexical reuse from context while balancing relevance and fluency.\n2) The proposed CP-Order, CP-Link, and CP-Refine span hard to soft constraints, making the Stage 1 data diverse yet biased toward high copying.\n3) The authors also show that how by using with just 365 pairs for DPO, CopyPasteLLM outperforms Context-DPO trained on 18k pairs and other fine-tuning baselines across counterfactual settings, with large margins on FaithEval; non-counterfactual accuracy also improves", "weaknesses": "1) Lexical copying is not faithfulness, κ, δ reward verbatim reuse and long spans and a response can copy irrelevant or misleading context sentences which yet still be unfaithful to the question. The paper partially addresses relevance via embeddings and perplexity, but κ, δ might elevate answers that are faithful to the wrong passage.\n2) Stage 1 filtering and Stage 2 tournament rely on AlignScore, MiniCheck, and a Qwen-32B judge, plus embedding similarity for relevance. These components are themselves models with biases and variance, having inter annotator agreement with human would be essential.\n3) The “stamping” step may entangle selection by copying degree with label assignment. Without ablations, it’s hard to tell whether gains come from copying behavior or from aggressive negative construction.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "w8j2DtjTzO", "reviewer": "Reviewer_S4zF", "summary": "This paper proposes CopyPasteLLM, a two-stage framework to mitigate contextual faithfulness hallucinations in retrieval-augmented generation (RAG) systems. The authors observe an inverse correlation between copying degree and hallucination density, motivating them to develop methods that encourage high-copying behavior. The first stage generates high-copying responses through three prompting paradigms (CP-Order, CP-Link, CP-Refine), while the second stage uses these responses as preference data for Direct Preference Optimization (DPO) training. The method achieves 12.2%-24.5% improvements on FaithEval using only 365 training samples. The authors also propose a Context-Parameter Copying Capturing algorithm that reveals the model recalibrates reliance on parametric knowledge rather than enhancing contextual representations.", "questions": "1. **Rationale for Training Data Size:** The choice of 365 training samples is quite specific. Could the authors clarify if this number has a particular significance (e.g., the size of a specific training split from a source dataset)? More importantly, have the authors investigated the performance scaling of CopyPasteLLM with respect to the number of preference pairs? It would be interesting to see if performance continues to climb with more data (e.g., 1k or 5k samples) or if it plateaus quickly, which would further underscore the method's data efficiency.\n2. **Generalizability Beyond RAG:** The copy-paste mechanism is inherently tied to the presence of a source context. Could the authors elaborate on the potential generalizability of this approach beyond the classic RAG setting? For instance, could the core principle be adapted to other tasks where faithfulness to a source is paramount, such as abstractive summarization (remaining faithful to the original article) or long-form dialogue (remaining consistent with conversation history)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Strong empirical results with exceptional data efficiency**: The method achieves significant improvements (12.2%-24.5% on FaithEval) using only 365 training samples, which is 50× fewer than the strongest baseline (Context-DPO with 18,000 samples).\n2. **Valuable empirical observation**: The inverse correlation between copying degree and hallucination density provides an interesting insight that could inspire further research in this area.\n3. **Comprehensive experimental evaluation**: The paper evaluates on multiple datasets (RAGTruth, FaithEval, PubMedQA, ConFiQA) across both counterfactual and original contexts, with multiple model architectures.\n4. **Practical and implementable approach**: The automated preference data construction pipeline and straightforward copying mechanisms make the method accessible for practical deployment.\n5. **Mechanistic insights**: The Context-Parameter Copying Capturing algorithm provides interpretable analysis of how the model balances contextual versus parametric knowledge during generation.", "weaknesses": "1. **Idealized Assumption of Infallible Context:** The framework operates under the strong assumption that the retrieved context is always correct and sufficient. In real-world applications, retrieved information can be inaccurate, biased, or incomplete. By training the model to trust the context unconditionally, this method might inadvertently discourage a crucial capability: the ability to critically assess or question a flawed context. Treating the context as absolute truth may not always be desirable for robust and reliable AI systems.\n2. **Reliance on Lexical Metrics for Semantic Faithfulness:** The core metrics for copying, copy coverage ($\\kappa$) and copy density ($\\delta$), are based on lexical overlap. While empirically shown to be effective here, faithfulness is ultimately a semantic concept. It is possible for a response to have low lexical overlap but be a perfect, faithful paraphrase. Conversely, a response with high lexical overlap could subtly alter the meaning by changing a single critical word (e.g., \"not,\" \"only\"). The reliance on a lexical proxy may not fully capture the nuances of semantic fidelity.\n3. **Lack of Deeper Theoretical Grounding:** The paper presents a strong empirical correlation between high copy-degree and reduced hallucination but lacks a theoretical analysis to explain this causation more formally. Following the previous point, it would be beneficial to discuss why forcing lexical copying successfully translates into a preference for semantic faithfulness at a deeper level. How would this approach compare, for instance, to an alternative that uses a semantic similarity metric (instead of lexical overlap) as its primary optimization target during the preference generation stage (or combine lexical overlap and semantic similarity metric)?", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)