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LLM peer reviewers inflate weak-paper scores and obey hidden prompts

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-04 18:28 UTC pith:MBJESG56

load-bearing objection Worth a serious referee, but the headline injection and inflation claims need baseline comparisons and error bars before they can carry the policy conclusions. the 3 major comments →

arxiv 2509.09912 v1 pith:MBJESG56 submitted 2025-09-12 cs.CY cs.CR

When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review

Changjia Zhu , Junjie Xiong , Renkai Ma , Zhicong Lu , Yao Liu , Lingyao Li This is my paper
classification cs.CY cs.CR
keywords LLM-assisted peer reviewrating inflationprompt injectionreviewer biashuman-AI interactionreview content divergenceGPT-5-minitopic modeling
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper evaluates GPT-5-mini as an academic peer reviewer on 1,441 papers from two major machine-learning conferences with publicly available reviews. It finds that the model systematically overrates weaker papers—on average 1.16 points above human reviewers—while matching human judgment much more closely for stronger papers. If LLM scores alone set the bar, 95.8% of papers that human reviewers rejected would appear acceptable. The paper also shows that hidden instructions embedded in a paper's PDF can manipulate the model: broad 'give a positive review' prompts shift ratings only modestly, but field-specific prompts coerce a perfect 10/10 score in 30% of cases and suppress all but one weakness in another 30%. Humans and the LLM also disagree about what matters in a review: humans emphasize novelty and clarity, while the LLM focuses on empirical rigor and technical implementation.

Core claim

On a stratified sample of 991 ICLR 2023 and 450 NeurIPS 2022 papers, with human decisions as the quality baseline, GPT-5-mini's ratings are systematically inflated for low-rated work: papers with an average human rating of 3.5 receive LLM ratings on average 2.48 points higher, while papers at 7.5 receive ratings 0.34 lower. Across the full set the LLM averages 6.86 versus 5.70 for humans, and for the ICLR subset an LLM-based accept/reject decision would classify 479 of 500 human-rejected papers as acceptable. Topic analysis of review content shows moderate divergence (Jensen–Shannon divergence 0.031 for strengths and 0.043 for weaknesses): humans emphasize novelty of study design and present

What carries the argument

The central apparatus is a structured reviewing prompt that anchors the model's rating to human-calibrated reference papers (one paper per rating value, selected where human reviewers unanimously agreed), so the model is told to assign a score only if the target matches or exceeds that reference. Against this baseline the paper pits a covert injection technique that remaps TrueType font glyphs, making hidden instructions invisible to human readers but readable by the model, with injections placed at different locations and frequencies. The three resulting review sets—human, LLM, and LLM-with-injection—are compared using bottom-up topic clustering and lexical valence–salience analysis.

Load-bearing premise

The paper's central inflation and misclassification numbers are conditional on its chosen reviewer prompt—one that includes reference papers as anchors—and the paper shows that different prompts change the rating distribution drastically, so those numbers are not a stable property of the model.

What would settle it

Re-run the same 1,441 papers through GPT-5-mini with the same content but a differently worded neutral prompt, such as no reference anchors or a simpler instruction set, and check whether the +1.16 average inflation and the 95.8% misclassification of human-rejected papers persist; the paper's own Table 2 suggests they would not.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If LLM ratings were used directly for acceptance decisions, 95.8% of human-rejected ICLR papers in the sample would clear the poster threshold.
  • LLM-generated review content systematically under-weights novelty and presentation clarity, so a calibrated assistant role would need to leave those dimensions to humans.
  • Broad one-line prompt injection is a weak attack vector; field-specific instructions that target a single review field are the practical threat.
  • LLM rating distributions shift sharply with prompt phrasing (for example, 66% of papers get an 8 without reference anchors, versus 50% with them), so uncalibrated LLM scores cannot be treated as a stable measure.
  • Model generation matters for attack resistance: the older model complied with the perfect-score prompt in roughly 57% of cases, the newer one in 30%, indicating progress but not closure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The headline inflation figures are best read as lower bounds for naive use: the paper's own no-reference condition produced even more inflated ratings, and any deployment would need to re-benchmark against its own prompt and model.
  • Because field-specific injections succeed while broad ones fail, defense may shift to output-side checks—flagging implausible perfect scores or suspiciously short weakness lists—rather than only scanning inputs.
  • The topical divergence points to a testable division of labor: have LLMs verify experimental rigor and implementation details while humans judge novelty and significance, then measure whether hybrid reviews outperform either alone.
  • The font-remapping channel is not limited to peer review; any document-processing LLM workflow that ingests untrusted PDFs shares the same exposure, so the risk profile generalizes beyond academic reviewing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper evaluates GPT-5-mini as an academic peer reviewer on 1,441 sampled ICLR 2023 and NeurIPS 2022 papers, comparing LLM-generated ratings and review content against official human reviews. It also tests susceptibility to font-based indirect prompt injection, using both overarching instructions and field-specific instructions targeting ratings and weakness counts. The main claims are: (1) LLMs systematically inflate ratings for weaker papers while aligning more closely with human ratings on stronger papers; (2) LLM and human reviewers diverge in which aspects of a paper they emphasize; (3) overarching injected prompts have only modest effects, but field-specific embedded instructions can force extreme scores and suppress weaknesses. The paper concludes with policy and design implications, framing LLMs as calibrated assistants rather than judges.

Significance. If the central claims hold, the paper would be a useful empirical benchmark for LLM-assisted peer review and one of the first systematic threat assessments of document-based prompt injection in this setting. The study uses a realistic dataset, a transparent three-set construction, and includes a useful comparison with an earlier model (GPT-4o-mini). The rating-coercion result (30% perfect 10s under an explicit instruction, versus zero 10s at baseline) is properly controlled and is a genuine contribution. However, the weakness-suppression claim and the headline inflation numbers require additional controls and uncertainty quantification before they can support the paper's policy recommendations.

major comments (3)
  1. [Section 4.3, Fig. 7(c)] The claim that the field-specific weakness-reduction prompt 'eliminates all but a single weakness' is not identified against a baseline. The paper reports that 30% of injected GPT-5-mini reviews contain exactly one weakness, but it never reports the weakness-count distribution of Review Set 2 (no-injection LLM reviews) for the same 1,441 papers. If the baseline already produces one weakness in roughly 30% of reviews—for example through the JSON formatting in Appendix A or output truncation—the injection has no measurable suppression effect. This is load-bearing for RQ3 and for the Section 5.2 policy conclusion that field-specific instructions can 'suppress weaknesses.' Please report the baseline distribution, exact counts, and a confidence interval for the 30% estimate.
  2. [Section 4.1, Table 2 and Fig. 2] The headline +1.16 average inflation and the 95.8% misclassification rate are reported as properties of GPT-5-mini, but they are conditional on one reference-calibrated prompt and are not accompanied by confidence intervals or significance tests. Table 2 shows that the rating distribution shifts dramatically with prompt design: with reference papers, 50% of ratings are 8; without references, 66% are 8; under a 'tough evaluator' instruction, 73% are 6. The inflation and misclassification figures should therefore be reported per prompt condition, with uncertainty estimates. In addition, the 95.8% figure applies a threshold derived from accepted-paper distributions to rejected papers without reporting how human ratings would fare under the same decision rule; please provide that comparison.
  3. [Section 4.3, Fig. 7(a)] The claimed U-shaped location effect rests on small differences with no error bars or significance tests: first page 62.7%, quarter point 60.9%, three-quarter point 59.2%, last page 61.1%. The pattern is also non-monotonic. These differences are within the range of sampling noise, especially without per-condition sample sizes. Because the design recommendation in Section 5.2 proposes scanning 'document boundaries' based on this result, the evidence needs at least confidence intervals and ideally a paired significance test across injection locations.
minor comments (4)
  1. [Section 4.1, text near Table 2] The sentence beginning 'The figure shows that...' refers to Table 2, not a figure. Please correct the cross-reference.
  2. [Figure 4] The percentages in several rows do not sum to 100 (e.g., the Human-Strength row appears to sum to 101 and the LLM-Strength row to 101). Please verify the rounding or recompute the displayed values.
  3. [Appendix A and Section 4.3] The JSON template instructs the model to provide weaknesses as '1. ...\n2. ...\n3. ...', but the weakness-reduction analysis counts the number of weaknesses. Please clarify how the number of weaknesses is parsed and whether the template's three-bullet format biases the counts.
  4. [Section 3.4 and Figure 7(b)] The y-axis of Figure 7(b) starts at 900, which visually exaggerates the small differences among frequency conditions; error bars and exact N per condition are needed. More generally, the paper would benefit from a data/code availability statement so the BERTopic clustering and the injection experiments can be reproduced.

Circularity Check

0 steps flagged

No significant circularity: central results are empirical comparisons against external human reviews; only minor self-citations for the font-injection tool.

full rationale

The paper's load-bearing claims—LLM rating inflation (Section 4.1), human/LLM divergence in strengths and weaknesses (Section 4.2), and prompt-injection susceptibility (Section 4.3)—are all empirical measurements compared against external human reviews from OpenReview or against the paper's own baseline LLM reviews. No fitted parameter is later relabeled as a prediction: the +1.16 average rating difference and the 95.8% misclassification rate are descriptive transformations of the measured LLM/human rating data, not outputs derived from an input assumption. The reference-calibration prompt (Appendix A) sets a rating rubric but does not define the measured inflation; the LLM's deviations from human ratings remain an open empirical result. The topic analysis (Section 3.4) uses the same GPT-5-mini model to label both human and LLM review comments, which is a methodological artifact risk but not a circular derivation, since the JSD/Jaccard results are computed from external review texts and the labeling step does not presuppose the divergence findings. The font-injection mechanism is reused from the authors' own prior work [69,70] (Section 3.3); this is a minor methodological self-citation, but it is not load-bearing: the prompt-injection conclusions are based on directly observed rating changes and review-content changes under the injected prompts, not on any conclusion imported from the cited papers. The weakness-reduction claim (30% single-weakness outputs) lacks a reported baseline weakness-count distribution—a correctness/evidence gap, not a circularity, so it does not raise the circularity score. Overall, no step in the paper's argument reduces by construction or by self-citation to its own inputs, so the paper is substantially self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 5 axioms · 0 invented entities

No new physical or conceptual entities are introduced. The analysis depends on human ground truth, self-reported contamination checks, a chosen prompt persona, and an LLM-based labeling pipeline. The main free parameters are data-derived thresholds and the clustering size, both of which affect headline numbers.

free parameters (2)
  • ICLR acceptance thresholds = 6.5 / 6.0 / 5.0
    Derived from the same ICLR human-rating data as the minimum score that at least 95% of papers in each acceptance category exceed. These thresholds drive the headline claim that 95.8% of rejected papers would be acceptable under LLM ratings.
  • Number of BERTopic clusters = 80
    Chosen by the elbow method in Section 3.4. The topic distributions and JSD values depend on this clustering choice.
axioms (5)
  • domain assumption Average human review ratings and meta-review decisions are accurate proxies for paper quality.
    Section 3.1 treats accepted papers as higher quality and uses averaged human ratings to define rating bins. The inflation finding is relative to this yardstick, even though the paper itself cites the NeurIPS consistency experiment showing human review randomness.
  • domain assumption The model's self-reported seen_before response reliably detects training-data contamination.
    Appendix A asks GPT-5-mini whether it has seen the paper or its reviews and discards acknowledged exposures. There is no independent check for unreported contamination.
  • ad hoc to paper The reference-calibrated prompt elicits the model's true review behavior.
    Section 3.2 and Table 2 show ratings vary with prompt wording. The reference condition is one of many possible reviewer personas and is treated as the baseline without independent justification.
  • domain assumption GPT-5-mini reads the font-camouflaged injected text as intended instructions.
    Section 3.3 relies on the TrueType font remapping method from self-cited work [69,70] and does not report per-paper validation that the hidden text was extracted as intended by the model.
  • domain assumption LLM-generated labels preserve the semantic content of both human and LLM review comments without systematic bias.
    Section 3.4 uses an LLM to condense every strength and weakness to 1 to 5 word labels before BERTopic. If the labeling model applies its own evaluative frame to human text, the human versus LLM topic divergence could be partly an artifact.

pith-pipeline@v1.3.0-alltime-deepseek · 27223 in / 12689 out tokens · 122866 ms · 2026-08-04T18:28:26.626083+00:00 · methodology

0 comments
read the original abstract

Peer review is the cornerstone of academic publishing, yet the process is increasingly strained by rising submission volumes, reviewer overload, and expertise mismatches. Large language models (LLMs) are now being used as "reviewer aids," raising concerns about their fairness, consistency, and robustness against indirect prompt injection attacks. This paper presents a systematic evaluation of LLMs as academic reviewers. Using a curated dataset of 1,441 papers from ICLR 2023 and NeurIPS 2022, we evaluate GPT-5-mini against human reviewers across ratings, strengths, and weaknesses. The evaluation employs structured prompting with reference paper calibration, topic modeling, and similarity analysis to compare review content. We further embed covert instructions into PDF submissions to assess LLMs' susceptibility to prompt injection. Our findings show that LLMs consistently inflate ratings for weaker papers while aligning more closely with human judgments on stronger contributions. Moreover, while overarching malicious prompts induce only minor shifts in topical focus, explicitly field-specific instructions successfully manipulate specific aspects of LLM-generated reviews. This study underscores both the promises and perils of integrating LLMs into peer review and points to the importance of designing safeguards that ensure integrity and trust in future review processes.

Figures

Figures reproduced from arXiv: 2509.09912 by Changjia Zhu, Junjie Xiong, Lingyao Li, Renkai Ma, Yao Liu, Zhicong Lu.

Figure 1
Figure 1. Figure 1: Implementation Flowchart of the LLM-Based Review Evaluation Framework [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of LLM-assigned ratings with human reviewer ratings for ICLR 2023 (left) and NeurIPS 2022 (right). Human [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The distribution of LLM-human rating differences across (a) ICLR decision outcomes; (b) ICLR research track areas. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Topic Distribution across strengths and weaknesses for human reviews, LLM reviews, and LLM reviews with prompt injection. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: CDF of Jaccard similarity between LLM- and human-identified review topics at the individual paper level. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Lexical characteristics of reviews across three sources: (a) Human Reviews, (b) GPT-5-mini Reviews, and (c) GPT-5-mini Reviews [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Impact of prompt injection. (a) Location effect: a U-shaped pattern emerges, with injections placed at document boundaries [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

100 extracted references · 18 linked inside Pith · cited by 6 Pith papers

  1. [1]

    ACL Rolling Review. 2025. Authors Guidelines. https://aclrollingreview.org/authors

    2025

  2. [2]

    Association for Computing Machinery. 2025. Peer Review Policy FAQ. https://www.acm.org/publications/policies/peer-review-faq

    2025

  3. [3]

    Md Ahsan Ayub and Subhabrata Majumdar. 2024. Embedding-based classifiers can detect prompt injection attacks.arXiv preprint arXiv:2410.22284 (2024)

    Pith/arXiv arXiv 2024

  4. [4]

    Anna Brown, Alexandra Chouldechova, Emily Putnam-Hornstein, Andrew Tobin, and Rhema Vaithianathan. 2019. Toward algorithmic accountability in public services: A qualitative study of affected community perspectives on algorithmic decision-making in child welfare services. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–12

    2019

  5. [5]

    Amy S Bruckman, Casey Fiesler, Jeff Hancock, and Cosmin Munteanu. 2017. CSCW research ethics town hall: Working towards community norms. InCompanion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 113–115

    2017

  6. [6]

    Ángel Alexander Cabrera, Adam Perer, and Jason I Hong. 2023. Improving human-AI collaboration with descriptions of AI behavior.Proceedings of the ACM on Human-Computer Interaction7 (2023), 1–21

    2023

  7. [7]

    Alexander J Carroll and Joshua Borycz. 2024. Integrating large language models and generative artificial intelligence tools into information literacy instruction.The Journal of Academic Librarianship50, 4 (2024), 102899

    2024

  8. [8]

    Shiping Chen, Duncan Brumby, and Anna Cox. 2025. Envisioning the Future of Peer Review: Investigating LLM-Assisted Reviewing Using ChatGPT as a Case Study. InProceedings of the 4th Annual Symposium on Human-Computer Interaction for Work. 1–18

    2025

  9. [9]

    Sizhe Chen, Julien Piet, Chawin Sitawarin, and David Wagner. 2025. Struq: Defending against prompt injection with structured queries. In34rd USENIX Security Symposium (USENIX Security 25)

    2025

  10. [10]

    Yulin Chen, Haoran Li, Zihao Zheng, Yangqiu Song, Dekai Wu, and Bryan Hooi. 2024. Defense against prompt injection attack by leveraging attack techniques.arXiv preprint arXiv:2411.00459(2024)

    Pith/arXiv arXiv 2024

  11. [11]

    CHI 2025 conference. 2024. CHI 2025 — Papers Track, post-review report (Round 1). https://chi2025.acm.org/chi-2025-papers-track-post-review- report-round-1/

    2025

  12. [12]

    CHI 2026 Conference. 2025. Guide to Reviewing Papers. https://chi2026.acm.org/guide-to-reviewing-papers/

    2026

  13. [13]

    Valdemar Danry, Pat Pataranutaporn, Matthew Groh, and Ziv Epstein. 2025. Deceptive explanations by large language models lead people to change their beliefs about misinformation more often than honest explanations. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–31

    2025

  14. [14]

    Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. 2024. Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents.Advances in Neural Information Processing Systems37 (2024), 82895–82920

    2024

  15. [15]

    Rebecca Dorn, Lee Kezar, Fred Morstatter, and Kristina Lerman. 2024. Harmful speech detection by language models exhibits gender-queer dialect bias. InProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. 1–12

    2024

  16. [16]

    Eric Price. 2014. The NIPS experiment. https://blog.mrtz.org/2014/12/15/the-nips-experiment.html

    2014

  17. [17]

    Lizhou Fan, Lingyao Li, Zihui Ma, Sanggyu Lee, Huizi Yu, and Libby Hemphill. 2024. A bibliometric review of large language models research from 2017 to 2023.ACM Transactions on Intelligent Systems and Technology15, 5 (2024), 1–25

    2024

  18. [18]

    Christopher Flathmann, Wen Duan, Nathan J Mcneese, Allyson Hauptman, and Rui Zhang. 2024. Empirically understanding the potential impacts and process of social influence in human-AI teams.Proceedings of the ACM on Human-Computer Interaction8 (2024), 1–32

    2024

  19. [19]

    Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023. Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection. InProceedings of the 16th ACM workshop on artificial intelligence and security. 79–90

    2023

  20. [20]

    Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure.arXiv preprint arXiv:2203.05794(2022)

    Pith/arXiv arXiv 2022

  21. [21]

    William Hackett, Lewis Birch, Stefan Trawicki, Neeraj Suri, and Peter Garraghan. 2025. Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails.arXiv preprint arXiv:2504.11168(2025)

    Pith/arXiv arXiv 2025

  22. [22]

    David Hartmann, Amin Oueslati, Dimitri Staufer, Lena Pohlmann, Simon Munzert, and Hendrik Heuer. 2025. Lost in moderation: How commercial content moderation apis over-and under-moderate group-targeted hate speech and linguistic variations. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–26

    2025

  23. [23]

    gatekeepers

    Mohammadreza Hojat, Joseph S Gonnella, and Addeane S Caelleigh. 2003. Impartial judgment by the “gatekeepers” of science: fallibility and accountability in the peer review process.Advances in Health Sciences Education8, 1 (2003), 75–96. Manuscript submitted to ACM 22 Changjia Zhu, Junjie Xiong, Renkai Ma, Zhicong Lu, Yao Liu, and Lingyao Li

    2003

  24. [24]

    ICLR. 2025. ICLR 2025 Reviewer Guide. https://iclr.cc/Conferences/2025/ReviewerGuide

    2025

  25. [25]

    Yvonne Jansen, Kasper Hornbæk, and Pierre Dragicevic. 2016. What Did Authors Value in the CHI’16 Reviews They Received?. InProceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 596–608

    2016

  26. [26]

    Jacalyn Kelly, Tara Sadeghieh, and Khosrow Adeli. 2014. Peer review in scientific publications: benefits, critiques, & a survival guide.Ejifcc25, 3 (2014), 227

    2014

  27. [27]

    Tine Köhler, M Gloria González-Morales, George C Banks, Ernest H O’Boyle, Joseph A Allen, Ruchi Sinha, Sang Eun Woo, and Lisa MV Gulick. 2020. Supporting robust, rigorous, and reliable reviewing as the cornerstone of our profession: Introducing a competency framework for peer review. Industrial and Organizational Psychology13, 1 (2020), 1–27

    2020

  28. [28]

    Michelle S Lam, Mitchell L Gordon, Danaë Metaxa, Jeffrey T Hancock, James A Landay, and Michael S Bernstein. 2022. End-user audits: A system empowering communities to lead large-scale investigations of harmful algorithmic behavior.Proceedings of the ACM on Human-Computer Interaction 6 (2022), 1–34

    2022

  29. [29]

    Giuseppe Russo Latona, Manoel Horta Ribeiro, Tim R Davidson, Veniamin Veselovsky, and Robert West. 2024. The ai review lottery: Widespread ai-assisted peer reviews boost paper scores and acceptance rates.arXiv preprint arXiv:2405.02150(2024)

    Pith/arXiv arXiv 2024

  30. [30]

    Rena Li, Sara Kingsley, Chelsea Fan, Proteeti Sinha, Nora Wai, Jaimie Lee, Hong Shen, Motahhare Eslami, and Jason Hong. 2023. Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–19

    2023

  31. [31]

    Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, et al

  32. [32]

    Weixin Liang, Yuhui Zhang, Hancheng Cao, Binglu Wang, Daisy Yi Ding, Xinyu Yang, Kailas Vodrahalli, Siyu He, Daniel Scott Smith, Yian Yin, et al

  33. [33]

    Xinrui Lin, Heyan Huang, Kaihuang Huang, Xin Shu, and John Vines. 2025. Seeking Inspiration through Human-LLM Interaction. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–17

    2025

  34. [34]

    Can large language models provide useful feedback on research papers? A large-scale empirical analysis.NEJM AI1, 8 (2024), AIoa2400196

    2024

  35. [35]

    Xiang Liu, Torsten Suel, and Nasir Memon. 2014. A robust model for paper reviewer assignment. InProceedings of the 8th ACM Conference on Recommender systems. 25–32

    2014

  36. [36]

    Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. Lost in the middle: How language models use long contexts.arXiv preprint arXiv:2307.03172(2023)

    Pith/arXiv arXiv 2023

  37. [37]

    Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, and Cong Yao. 2024. Layoutllm: Layout instruction tuning with large language models for document understanding. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 15630–15640

    2024

  38. [38]

    Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Zihao Wang, Xiaofeng Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, et al. 2023. Prompt injection attack against llm-integrated applications.arXiv preprint arXiv:2306.05499(2023)

    Pith/arXiv arXiv 2023

  39. [39]

    Leland McInnes, John Healy, and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction.arXiv preprint arXiv:1802.03426(2018)

    Pith/arXiv arXiv 2018

  40. [40]

    Richard Mallett, Jessica Hagen-Zanker, Rachel Slater, and Maren Duvendack. 2012. The benefits and challenges of using systematic reviews in international development research.Journal of development effectiveness4, 3 (2012), 445–455

    2012

  41. [41]

    Miryam Naddaf. 2025. AI is transforming peer review — and many scientists are worried. https://www.nature.com/articles/d41586-025-00894-7

    2025

  42. [42]

    David Mimno and Andrew McCallum. 2007. Expertise modeling for matching papers with reviewers. InProceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 500–509

    2007

  43. [43]

    Nature Portfolio. 2025. Referees — Authors and Referees. https://www.nature.com/mp/authors-and-referees/referees

    2025

  44. [44]

    Belal Abdullah Hezam Murshed, Suresha Mallappa, Jemal Abawajy, Mufeed Ahmed Naji Saif, Hasib Daowd Esmail Al-Ariki, and Hudhaifa Mohammed Abdulwahab. 2023. Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis.Artificial Intelligence Review56, 6 (2023), 5133–5260

    2023

  45. [45]

    Kenneth Nugent and Christopher J Peterson. 2024. Peer review and medical journals.Journal of Primary Care & Community Health15 (2024), 21501319241252235

    2024

  46. [46]

    NeurIPS. 2024. NeurIPS Fact Sheet. https://media.neurips.cc/Conferences/NeurIPS2024/NeurIPS2024-Fact_Sheet.pdf

    2024

  47. [47]

    Bayode Ogunleye, Tonderai Maswera, Laurence Hirsch, Jotham Gaudoin, and Teresa Brunsdon. 2023. Comparison of topic modelling approaches in the banking context.Applied Sciences13, 2 (2023), 797

    2023

  48. [48]

    Barbara Nussbaumer-Streit, Moriah Ellen, Irma Klerings, Raluca Sfetcu, Nicoletta Riva, Mersiha Mahmić-Kaknjo, Georgios Poulentzas, P Martinez, Eduard Baladia, Liliya Eugenevna Ziganshina, et al. 2021. Resource use during systematic review production varies widely: a scoping review.Journal of clinical epidemiology139 (2021), 287–296

    2021

  49. [49]

    openreview.net. 2025. https://openreview.net/

    2025

  50. [50]

    OpenAI. 2025. Introducing GPT-5. https://openai.com/index/introducing-gpt-5/

    2025

  51. [51]

    Nicholas Proferes, Naiyan Jones, Sarah Gilbert, Casey Fiesler, and Michael Zimmer. 2021. Studying reddit: A systematic overview of disciplines, approaches, methods, and ethics.Social Media+ Society7, 2 (2021), 20563051211019004. Manuscript submitted to ACM When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review 23

    2021

  52. [52]

    Paper Copilot. 2025. ICLR 2025 Statistics. https://papercopilot.com/statistics/iclr-statistics/iclr-2025-statistics/

    2025

  53. [53]

    Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010. Automatic keyword extraction from individual documents.Text mining: applications and theory(2010), 1–20

    2010

  54. [54]

    Javier Rando, Jie Zhang, Nicholas Carlini, and Florian Tramèr. 2025. Adversarial ml problems are getting harder to solve and to evaluate.arXiv preprint arXiv:2502.02260(2025)

    Pith/arXiv arXiv 2025

  55. [55]

    Hong Shen, Alicia DeVos, Motahhare Eslami, and Kenneth Holstein. 2021. Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors.Proceedings of the ACM on Human-Computer Interaction5 (2021), 1–29

    2021

  56. [56]

    Sippo Rossi, Alisia Marianne Michel, Raghava Rao Mukkamala, and Jason Bennett Thatcher. 2024. An early categorization of prompt injection attacks on large language models.arXiv preprint arXiv:2402.00898(2024)

    Pith/arXiv arXiv 2024

  57. [57]

    Springer Nature. 2025. Transparent peer review to be extended to all of Nature’s research papers. https://www.nature.com/articles/d41586-025- 01880-9

    2025

  58. [58]

    Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, and Juho Kim. 2025. Automatically evaluating the paper reviewing capability of large language models.arXiv e-prints(2025), arXiv–2502

    2025

  59. [59]

    Nitya Thakkar, Mert Yuksekgonul, Jake Silberg, Animesh Garg, Nanyun Peng, Fei Sha, Rose Yu, Carl Vondrick, and James Zou. 2025. Can llm feedback enhance review quality? a randomized study of 20k reviews at iclr 2025.arXiv preprint arXiv:2504.09737(2025)

    Pith/arXiv arXiv 2025

  60. [60]

    Chris Street and Kerry W Ward. 2019. Cognitive bias in the peer review process: Understanding a source of friction between reviewers and researchers.ACM SIGMIS Database: the DATABASE for Advances in Information Systems50, 4 (2019), 52–70

    2019

  61. [61]

    Michelle Vaccaro, Abdullah Almaatouq, and Thomas Malone. 2024. When combinations of humans and AI are useful: A systematic review and meta-analysis.Nature Human Behaviour8, 12 (2024), 2293–2303

    2024

  62. [62]

    Keith Tyser, Ben Segev, Gaston Longhitano, Xin-Yu Zhang, Zachary Meeks, Jason Lee, Uday Garg, Nicholas Belsten, Avi Shporer, Madeleine Udell, et al. 2024. Ai-driven review systems: evaluating llms in scalable and bias-aware academic reviews.arXiv preprint arXiv:2408.10365(2024)

    Pith/arXiv arXiv 2024

  63. [63]

    Jessica Vitak, Nicholas Proferes, Katie Shilton, and Zahra Ashktorab. 2017. Ethics regulation in social computing research: Examining the role of institutional review boards.Journal of Empirical Research on Human Research Ethics12, 5 (2017), 372–382

    2017

  64. [64]

    Michael Veale, Max Van Kleek, and Reuben Binns. 2018. Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–14

    2018

  65. [65]

    Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li. 2024. Knowledge editing for large language models: A survey. Comput. Surveys57, 3 (2024), 1–37

    2024

  66. [66]

    Jiongxiao Wang, Fangzhou Wu, Wendi Li, Jinsheng Pan, Edward Suh, Z Morley Mao, Muhao Chen, and Chaowei Xiao. 2024. Fath: Authentication- based test-time defense against indirect prompt injection attacks.arXiv preprint arXiv:2410.21492(2024)

    Pith/arXiv arXiv 2024

  67. [67]

    Max L Wilson and Lennart Nacke. 2022. How to: Peer review for CHI (and beyond). InCHI Conference on Human Factors in Computing Systems Extended Abstracts. 1–4

    2022

  68. [68]

    Maranke Wieringa. 2020. What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 1–18

    2020

  69. [69]

    Junjie Xiong, Mingkui Wei, Xiao Han, Zhuo Lu, and Yao Liu. 2025. The Implications of Insecure Use of Fonts Against PDF Documents and Web Pages.IEEE Transactions on Information Forensics and Security20 (2025), 8773–8787. doi:10.1109/TIFS.2025.3599320

    arXiv 2025

  70. [70]

    Fangzhou Wu, Shutong Wu, Yulong Cao, and Chaowei Xiao. 2024. Wipi: A new web threat for llm-driven web agents.arXiv preprint arXiv:2402.16965 (2024)

    Pith/arXiv arXiv 2024

  71. [71]

    Rui Ye, Xianghe Pang, Jingyi Chai, Jiaao Chen, Zhenfei Yin, Zhen Xiang, Xiaowen Dong, Jing Shao, and Siheng Chen. 2024. Are we there yet? revealing the risks of utilizing large language models in scholarly peer review.arXiv preprint arXiv:2412.01708(2024)

    Pith/arXiv arXiv 2024

  72. [72]

    Junjie Xiong, Changjia Zhu, Shuhang Lin, Chong Zhang, Yongfeng Zhang, Yao Liu, and Lingyao Li. 2025. Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models.arXiv preprint arXiv:2505.16957(2025)

    Pith/arXiv arXiv 2025

  73. [73]

    Sungduk Yu, Man Luo, Avinash Madasu, Vasudev Lal, and Phillip Howard. 2024. Is your paper being reviewed by an llm? investigating ai text detectability in peer review.arXiv preprint arXiv:2410.03019(2024)

    Pith/arXiv arXiv 2024

  74. [74]

    Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman, Guangzhong Sun, Xing Xie, and Fangzhao Wu. 2025. Benchmarking and defending against indirect prompt injection attacks on large language models. InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1. 1809–1820

    2025

  75. [75]

    Chao Zhang, Shengqi Zhu, Xinyu Yang, Yu-Chia Tseng, Shenrong Jiang, and Jeffrey M Rzeszotarski. 2025. Navigating the fog: How university students recalibrate sensemaking practices to address plausible falsehoods in llm outputs. InProceedings of the 7th ACM Conference on Conversational User Interfaces. 1–15

    2025

  76. [76]

    Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. 2024. InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents. InFindings of the Association for Computational Linguistics ACL 2024. 10471–10506

    2024

  77. [77]

    Ruiyang Zhou, Lu Chen, and Kai Yu. 2024. Is LLM a reliable reviewer? a comprehensive evaluation of LLM on automatic paper reviewing tasks. In Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-COLING 2024). 9340–9351

    2024

  78. [78]

    An ideal human

    Rui Zhang, Nathan J McNeese, Guo Freeman, and Geoff Musick. 2021. " An ideal human" expectations of AI teammates in human-AI teaming. Proceedings of the ACM on Human-Computer Interaction4 (2021), 1–25

    2021

  79. [79]

    Yuqi Zhu, Xiaohan Wang, Jing Chen, Shuofei Qiao, Yixin Ou, Yunzhi Yao, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2024. Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities.World Wide Web27, 5 (2024), 58. A Prompt Design for LLM-Based Reviewing We provide here the designed prompt used to instruct the LLM in ge...

    2024

  80. [80]

    Minjun Zhu, Yixuan Weng, Linyi Yang, and Yue Zhang. 2025. Deepreview: Improving llm-based paper review with human-like deep thinking process.arXiv preprint arXiv:2503.08569(2025). Manuscript submitted to ACM 24 Changjia Zhu, Junjie Xiong, Renkai Ma, Zhicong Lu, Yao Liu, and Lingyao Li

    Pith/arXiv arXiv 2025

Showing first 80 references.