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arxiv: 2604.06393 · v1 · submitted 2026-04-07 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

ART: Attention Replacement Technique to Improve Factuality in LLMs

Chen Shen, Xiaofeng Zhang, Xiaosong Yuan, Yihao Quan, Ziqin Luo

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:44 UTC · model grok-4.3

classification 💻 cs.CL
keywords attention patternshallucinationslarge language modelsfactualitytraining-freeshallow layerslocal attention
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The pith

Replacing uniform attention in shallow LLM layers with local patterns reduces hallucinations without training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models tend to generate false information because their shallow layers distribute attention uniformly across the entire input sequence instead of concentrating on important details. The authors identify this uniform pattern as a key driver of hallucinations in tasks such as question answering. They introduce a simple replacement method that swaps the uniform attention for local attention patterns focused on nearby tokens in those early layers. Experiments across different models show this change lowers the rate of incorrect outputs while preserving general performance. The approach requires no retraining or extra data, making it easy to apply.

Core claim

Shallow layers of LLMs primarily rely on uniform attention patterns that distribute attention evenly across the sequence, causing the model to fail to focus on the most relevant information and thereby producing hallucinations. The Attention Replacement Technique replaces these uniform patterns with local attention patterns in the shallow layers, which directs the model to focus more on relevant contexts and reduces hallucinations.

What carries the argument

Attention Replacement Technique (ART) that identifies and replaces uniform attention patterns in shallow layers with local attention patterns.

Load-bearing premise

Uniform attention patterns in shallow layers directly cause hallucinations by preventing focus on relevant information, and local replacement will fix this without side effects.

What would settle it

Running the same evaluation benchmarks on models with and without the attention replacement and finding no decrease or an increase in hallucination rates.

Figures

Figures reproduced from arXiv: 2604.06393 by Chen Shen, Xiaofeng Zhang, Xiaosong Yuan, Yihao Quan, Ziqin Luo.

Figure 1
Figure 1. Figure 1: A demonstration overview of how ART works [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization on some of attention weights of [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The detailed demonstration of applying ART-max to the second layer of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The structure diagram of ART replaces the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Accuracy curves of Qwen2.5-7B-Instruct eval￾uated on Dablation of TruthfulQA and GSM8K subsets after masking different amounts of uniform, scattered, and local attention. to neighboring tokens; in scattered attention, each token focuses predominantly on certain preceding tokens. For better illustration, we define the m￾index ml h to classify these three types of attention. Formally, let U ∈ R T ×T denotes … view at source ↗
Figure 6
Figure 6. Figure 6: Accuracy curves of [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

Hallucination in large language models (LLMs) continues to be a significant issue, particularly in tasks like question answering, where models often generate plausible yet incorrect or irrelevant information. Although various methods have been proposed to mitigate hallucinations, the relationship between attention patterns and hallucinations has not been fully explored. In this paper, we analyze the distribution of attention scores across each layer and attention head of LLMs, revealing a common and intriguing phenomenon: shallow layers of LLMs primarily rely on uniform attention patterns, where the model distributes its attention evenly across the entire sequence. This uniform attention pattern can lead to hallucinations, as the model fails to focus on the most relevant information. To mitigate this issue, we propose a training-free method called Attention Replacement Technique (ART), which replaces these uniform attention patterns in the shallow layers with local attention patterns. This change directs the model to focus more on the relevant contexts, thus reducing hallucinations. Through extensive experiments, ART demonstrates significant reductions in hallucinations across multiple LLM architectures, proving its effectiveness and generalizability without requiring fine-tuning or additional training data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that shallow layers of LLMs exhibit uniform attention patterns that cause hallucinations by failing to focus on relevant information. It proposes the training-free Attention Replacement Technique (ART) to replace these uniform patterns with local attention patterns in shallow layers, thereby directing attention to relevant contexts and reducing hallucinations. The paper asserts that extensive experiments demonstrate significant reductions in hallucinations across multiple LLM architectures without requiring fine-tuning or additional training data.

Significance. If the central claims hold, ART offers a lightweight, training-free intervention applicable to existing LLMs that could meaningfully improve factuality in tasks such as question answering. The approach is notable for its generality across architectures and lack of retraining requirements, which would make it practical for deployment.

major comments (2)
  1. [Abstract] Abstract: The abstract states that 'extensive experiments' show 'significant reductions in hallucinations' but supplies no metrics, baselines, statistical details, ablation results, or quantitative values, preventing assessment of whether the data support the headline claim.
  2. [Method/Experiments] Method and Experiments sections: The causal claim that uniform shallow-layer attention produces hallucinations (rather than being a downstream correlate) rests on observation plus post-intervention improvement. The manuscript does not report controls that substitute non-local but still non-uniform patterns, nor does it measure whether attention mass on ground-truth relevant spans increases after replacement; without these, the reduction could arise from any disruptive change to early-layer attention.
minor comments (1)
  1. [Abstract] Abstract: Adding at least one concrete quantitative result (e.g., hallucination rate reduction on a named benchmark) would strengthen the summary.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments on our manuscript. We address each of the major comments below in a point-by-point manner and indicate the changes we will make to the revised version.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract states that 'extensive experiments' show 'significant reductions in hallucinations' but supplies no metrics, baselines, statistical details, ablation results, or quantitative values, preventing assessment of whether the data support the headline claim.

    Authors: We concur that the abstract lacks specific quantitative details, which limits the ability to evaluate the strength of the claims. To address this, we will revise the abstract in the next version of the manuscript to incorporate key results, including average hallucination reduction percentages across models and tasks, comparisons with baseline methods, and references to the statistical significance of the improvements. This revision will maintain the abstract's conciseness while providing essential evidence. revision: yes

  2. Referee: [Method/Experiments] Method and Experiments sections: The causal claim that uniform shallow-layer attention produces hallucinations (rather than being a downstream correlate) rests on observation plus post-intervention improvement. The manuscript does not report controls that substitute non-local but still non-uniform patterns, nor does it measure whether attention mass on ground-truth relevant spans increases after replacement; without these, the reduction could arise from any disruptive change to early-layer attention.

    Authors: We appreciate this critique on establishing causality. The manuscript presents observational evidence of uniform attention in shallow layers and demonstrates that targeted replacement with local patterns reduces hallucinations. However, we acknowledge the absence of the suggested controls. In the revised manuscript, we will include additional experiments: an ablation study replacing shallow-layer attention with non-uniform but non-local patterns (such as randomly perturbed attention) to show that not all disruptions improve performance, and we will quantify the change in attention mass allocated to ground-truth relevant context spans before and after applying ART. These additions will help isolate the effect of the local pattern specifically. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical observation plus heuristic intervention

full rationale

The paper reports an empirical analysis of attention score distributions across layers and heads, notes the prevalence of uniform patterns in shallow layers, posits that these patterns hinder focus on relevant tokens, and introduces the ART replacement rule as a direct, training-free fix. No equations, fitted parameters, or derived predictions appear; the central claim rests on the observed distribution and the measured post-intervention hallucination drop rather than any self-referential definition or statistical forcing. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The chain is therefore self-contained observational work plus intervention testing and does not reduce any result to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unproven causal link between uniform shallow-layer attention and hallucinations plus the assumption that local replacement improves factuality without side effects.

axioms (1)
  • domain assumption Uniform attention patterns in shallow layers cause hallucinations by failing to focus on the most relevant information.
    Explicitly stated in the abstract as the motivation for the replacement technique.

pith-pipeline@v0.9.0 · 5493 in / 1137 out tokens · 35635 ms · 2026-05-10T19:44:00.041303+00:00 · methodology

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Lean theorems connected to this paper

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Works this paper leans on

16 extracted references · 15 canonical work pages · 3 internal anchors

  1. [1]

    Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Jun- yang Lin, Chang Zhou, and Baobao Chang

    Count- ing circuits: Mechanistic interpretability of visual reasoning in large vision-language models.arXiv preprint arXiv:2603.18523. Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Jun- yang Lin, Chang Zhou, and Baobao Chang. 2024a. An image is worth 1/2 tokens after layer 2: Plug-and- play inference acceleration for large vision-language models. InProc...

    work page arXiv

  2. [2]

    Training Verifiers to Solve Math Word Problems

    Training verifiers to solve math word prob- lems.arXiv preprint arXiv:2110.14168. Siqi Fan, Xin Jiang, Xiang Li, Xuying Meng, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang, and Zhongyuan Wang

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    Not all layers of LLMs are necessary during inference.arXiv preprint arXiv:2403.02181, 2024

    Not all layers of llms are necessary during inference.arXiv preprint arXiv:2403.02181. Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu

    work page arXiv

  4. [4]

    Zeru Shi, Kai Mei, Yihao Quan, Dimitris N Metaxas, and Ruixiang Tang

    Reinforcing consistency in video mllms with structured rewards.arXiv preprint arXiv:2604.01460. Zeru Shi, Kai Mei, Yihao Quan, Dimitris N Metaxas, and Ruixiang Tang

    work page arXiv

  5. [5]

    N., and Tang, R

    Improving visual reason- ing with iterative evidence refinement.arXiv preprint arXiv:2603.14117. Zeru Shi, Yingjia Wan, Zhenting Wang, Qifan Wang, Fan Yang, Elisa Kreiss, and Ruixiang Tang

    work page arXiv

  6. [6]

    Meaningless tokens, meaningful gains: How activation shifts enhance llm reasoning.arXiv preprint arXiv:2510.01032,

    Meaningless tokens, meaningful gains: How activa- tion shifts enhance llm reasoning.arXiv preprint arXiv:2510.01032. Qi Sun, Marc Pickett, Aakash Kumar Nain, and Llion Jones

    work page arXiv

  7. [7]

    Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant

    Transformer layers as painters.arXiv preprint arXiv:2407.09298. Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant

    work page arXiv

  8. [8]

    Llama 2: Open Foundation and Fine-Tuned Chat Models

    Llama 2: Open foundation and fine-tuned chat mod- els.arXiv preprint arXiv:2307.09288. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    InAd- vances in Neural Information Processing Systems 35 (NIPS)

    Chain-of-thought prompting elicits reasoning in large language models. InAd- vances in Neural Information Processing Systems 35 (NIPS). Jinfeng Wei and Xiaofeng Zhang. 2024a. Dopra: Decoding over-accumulation penalization and re- allocation in specific weighting layer.Proceedings of the 32nd ACM International Conference on Multi- media. Jinfeng Wei and Xi...

    work page arXiv

  10. [10]

    Rating: [[...]] Analysis:

    Retrieval head mechanisti- cally explains long-context factuality.arXiv preprint arXiv:2404.15574. Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Junxian Guo, Shang Yang, Haotian Tang, Yao Fu, and Song Han. 2024a. Duoattention: Efficient long-context llm inference with retrieval and streaming heads.arXiv preprint arXiv:2410.10819. Guangxuan Xiao, Yuandong Tia...

    work page arXiv

  11. [11]

    Qwen2 Technical Report

    Qwen2 technical report. arXiv preprint arXiv:2407.10671. Fangcong Yin, Xi Ye, and Greg Durrett

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J

    Lofit: Localized fine-tuning on llm representations.arXiv preprint arXiv:2406.01563. Zhongzhi Yu, Zheng Wang, Yonggan Fu, Huihong Shi, Khalid Shaikh, and Yingyan Celine Lin

    work page arXiv

  13. [13]

    Boxuan Zhang, Yi Yu, Jiaxuan Guo, and Jing Shao

    Instance-adaptive zero-shot chain-of-thought prompting.arXiv preprint arXiv:2409.20441. Boxuan Zhang, Yi Yu, Jiaxuan Guo, and Jing Shao. 2025a. Dive into the agent matrix: A realistic eval- uation of self-replication risk in llm agents.arXiv preprint arXiv:2509.25302. Boxuan Zhang and Ruqi Zhang

    work page arXiv

  14. [14]

    InFindings of the Associa- tion for Computational Linguistics: ACL 2025, pages 26114–26133

    Cot-uq: Improv- ing response-wise uncertainty quantification in llms with chain-of-thought. InFindings of the Associa- tion for Computational Linguistics: ACL 2025, pages 26114–26133. Xiaofeng Zhang, Yihao Quan, Chaochen Gu, Chen Shen, Xiaosong Yuan, Shaotian Yan, Hao Cheng, Kaijie Wu, and Jieping Ye

    2025

  15. [15]

    Seeing clearly by layer two: Enhancing attention heads to alleviate hallucination in lvlms.arXiv preprint arXiv:2411.09968, 2024

    Seeing clearly by layer two: Enhancing attention heads to al- leviate hallucination in lvlms.arXiv preprint arXiv:2411.09968. Xiaofeng Zhang, Yihao Quan, Chen Shen, Chaochen Gu, Xiaosong Yuan, Shaotian Yan, Jiawei Cao, Hao Cheng, Kaijie Wu, and Jieping Ye. 2025b. Shal- low focus, deep fixes: Enhancing shallow layers vi- sion attention sinks to alleviate h...

    work page arXiv 2025

  16. [16]

    Attention heads of large language models: A survey.arXiv preprint arXiv:2409.03752,

    Attention heads of large language models: A survey.arXiv preprint arXiv:2409.03752. Guanyu Zhou, Yibo Yan, Xin Zou, Kun Wang, Aiwei Liu, and Xuming Hu

    work page arXiv