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REVIEW 2 major objections 4 minor 1 cited by

Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?

T0 review · 2 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read For LLM reranking, reasoning is a liability

desk verdict A well-designed controlled comparison showing reasoning-based rerankers don't beat standard ones, but single-training-run noise means the closer margins are not settled; worth refereeing with a request for seed replicates. read the letter →

arxiv 2505.16886 v1 pith:FPJWHTCQ submitted 2025-05-22 cs.IR cs.AIcs.CLcs.LG

Nour Jedidi , Yung-Sung Chuang , James Glass , Jimmy Lin This is my paper
classification cs.IRcs.AIcs.CLcs.LG
keywords passagererankingreasoningmodelschain-of-thoughtpointwisererankerspartialrelevanceLLMinformationretrievaltest-timecomputeNDCG@10
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks whether generating a step-by-step chain of thought before scoring a query-passage pair actually improves reranking accuracy. It trains three variants under identical conditions — a standard pointwise reranker, a reasoning reranker, and a reasoning reranker with its reasoning process disabled at inference — and finds that the standard reranker generally wins, and that simply disabling reasoning often beats keeping it. The explanation offered is that the reasoning process pushes the model's relevance scores toward the extremes, destroying the graded, partial-relevance signal that pointwise reranking depends on. If correct, the practical consequence is that practitioners get better ranking accuracy at lower cost by skipping explicit reasoning in pointwise rerankers.

What carries the argument

The central object is the pointwise reranker's relevance score R, the softmax probability of the 'true' token over the logits of the 'true' and 'false' vocabulary tokens for a query-passage pair. The paper's key intervention is ReasonRR-NoReason, which disables the reasoning process at inference by pre-filling a forced reasoning placeholder (<think>Okay, I think I have finished thinking.</think>), turning a reasoning-trained model into a label-only scorer. Comparing the score distributions of these variants — and the classification-versus-ranking mismatch (ReasonRR is a better binary classifier but worse reranker) — is the mechanism through which the paper isolates the effect of reasoning on score polarization and partial-relevance modeling.

What would settle it

Train the same three reranker variants with, say, five random seeds per configuration. If the average ordering between StandardRR, ReasonRR, and ReasonRR-NoReason flips or the 0.5-point advantage disappears within seed variance, the central claim collapses. A second, mechanism-level test: measure the relevance-score distribution of a reasoning reranker fine-tuned with graded relevance labels; if it spreads mass across partial-relevance bins yet still underperforms StandardRR, the polarization explanation is wrong.

Watch

Extended reading notes

Core claim

Under identical training setups on the same Qwen2.5 backbones and the same MS MARCO-derived training data with DeepSeek-R1 reasoning chains, StandardRR generally outperforms ReasonRR both in-domain (TREC Deep Learning tracks) and out-of-domain (BRIGHT). Moreover, ReasonRR-NoReason, which pre-fills a forced reasoning placeholder at inference so the model only emits the relevance label, is more effective than ReasonRR on MS MARCO at all three scales and at 7B on BRIGHT. The paper's diagnosis is that the reasoning process polarizes relevance probabilities: scores concentrate near 0 and 1, with almost nothing in between, whereas the non-reasoning variants spread more mass across partial-relevance bins, which correlates with better NDCG@10. Self-consistency (averaging relevance probabilities over multiple sampled reasoning chains) recovers some partial-relevance mass and improves ReasonRR, but not enough to match StandardRR.

Load-bearing premise

The paper's ranking of methods rests on a single training run per model: Appendix C states that 'Due to limited computational resources, each model is only trained once,' so reported differences of 0.5 to 5 NDCG@10 points carry no error bars or significance tests.

Editorial extensions

If this is right

  • Practitioners using pointwise rerankers can expect higher NDCG@10 from a standard classifier-style reranker than from a chain-of-thought reranker trained on the same data and backbone.
  • Disabling a reasoning reranker's reasoning at inference (ReasonRR-NoReason) is a cheap way to recover some accuracy, especially at larger scales; on BRIGHT at 7B it nearly closes the gap to StandardRR.
  • Self-consistency sampling adds partial-relevance spread and improves reasoning rerankers, but does not make them competitive with the simple baseline.
  • Improvements to reasoning rerankers must explicitly reintroduce graded relevance, for example through non-binary training labels, calibrated loss functions, or extracting 'somewhat relevant' signals from the reasoning text.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If polarization is the true cause, then a reasoning reranker fine-tuned with a graded-label objective (for example 1 to 5 relevance) should recover partial-relevance spread and rival StandardRR; this is a direct testable extension the paper leaves open.
  • The finding likely transfers beyond pointwise reranking: setwise and listwise rerankers that compare multiple passages at once may be less affected by polarization because they can use relative comparisons, though the paper does not test this.
  • The forced-placeholder trick (ReasonRR-NoReason) is itself a useful diagnostic that could be applied to other reasoning-augmented tasks to separate the value of the trained reasoning response from the value of the label head.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. The paper investigates whether explicit reasoning chains improve pointwise passage reranking. It trains three reranker variants under identical conditions: StandardRR (direct relevance classification), ReasonRR (a reproduction of Rank1 that generates reasoning before predicting relevance), and ReasonRR-NoReason (ReasonRR with a forced placeholder that disables reasoning at inference). Evaluations on MS MARCO/TREC DL and BRIGHT show that StandardRR generally outperforms ReasonRR, and that ReasonRR-NoReason often outperforms ReasonRR. The authors attribute this to reasoning-induced polarization of relevance scores, which limits partial-relevance modeling, and show that self-consistency sampling partially mitigates the issue but does not close the gap.

Significance. If robust, the findings provide a valuable negative result for the IR community: explicit reasoning is not necessary for pointwise passage reranking, and simpler baselines are both more accurate and more cost-effective. The study is well controlled in that all variants share training data, backbone LLM family, LoRA hyperparameters, and evaluation pipeline, and the Rank1 reproduction is checked against reported numbers. The self-consistency experiment is a genuine intervention motivated by the polarization hypothesis rather than a restatement of the input. The principal weakness is the absence of multiple training runs or any statistical significance assessment, which limits the certainty of the quantitative comparisons.

major comments (2)
  1. [Appendix C; Tables 2 and 3] The central claims rest on NDCG@10 differences that are within the range of typical run-to-run variance for LoRA fine-tuning, yet each variant is trained only once. For example, the MS MARCO advantage of ReasonRR-NoReason over ReasonRR is 0.8, 0.5, and 1.4 points at 1.5B, 3B, and 7B respectively, and the StandardRR-versus-ReasonRR gap on BRIGHT at 3B is about 1 point. Appendix C states "Due to limited computational resources, each model is only trained once," and no standard errors, confidence intervals, or significance tests are reported anywhere. Without seed replicates, the ordering of methods, and particularly the small 0.5-point advantage at 3B on MS MARCO, may be due to seed luck rather than a true effect. Because the paper's headline negative conclusion ("reasoning is unnecessary") is an empirical claim, this lack of statistical support is load-bearing for the main argument.
  2. [Section 4.2 and Figure 2] The polarization explanation is derived from score distributions computed from the same single runs that produced the performance numbers. While the distribution shift between ReasonRR and ReasonRR-NoReason is large in the plotted example, no statistical quantification is provided (e.g., confidence intervals for the proportion of scores in the partial-relevance bins), and the causal statement that "the reasoning process leads the model to make polarized decisions" would be strengthened by demonstrating the effect is stable across seeds. This concern is secondary to the single-run issue but affects the interpretability of the proposed mechanism.
minor comments (4)
  1. [Table 4] There are formatting errors: "65.785.5 74.3" and "44.784.8 58.6" should presumably read "65.7 85.5 74.3" and "44.7 84.8 58.6". Please correct these values or add the missing spacing.
  2. [Abstract and Section 6] The conclusion states that "the reasoning process consistently harms the accuracy of pointwise rerankers," which overstates the results. Table 2 shows several domains (e.g., Psychology, StackOverflow, and AoPS at 1.5B and 3B) where ReasonRR outperforms StandardRR. Suggest using "on average" or "generally" instead of "consistently" to match the data.
  3. [Section 3.2 and Appendix G] The forced reasoning placeholder is presented in the main text as "Okay, I think I have finished thinking." but Appendix G shows "Okay, I have finished thinking." Please make the two consistent.
  4. [Section 4.1] The binary relevance threshold (R > 0.5) and the ground-truth threshold (judgments > 2) are stated, but it would help to note explicitly that these thresholds are standard practice and were not tuned on the evaluation sets.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's comparisons are empirical interventions, not derivations, and its reproduction is checked against external Rank1 results.

full rationale

This paper is an empirical comparison rather than a derivation chain. StandardRR, ReasonRR, and ReasonRR-NoReason are trained and evaluated on held-out MS MARCO and BRIGHT test sets; no quantity is fitted to a subset and then reported as a prediction of a closely related quantity. The central intervention, disabling reasoning by pre-filling a forced placeholder, is not equivalent to the input by construction: it changes the decoding trajectory while holding weights and training fixed, and its effect is measured on external test queries. The reproduction of Rank1 is validated against the published NDCG@10 (27.8 vs 27.5) using the same external benchmark, so the comparison carries independent support. The polarization explanation is a post-hoc hypothesis supported by score-distribution plots and a qualitative example, not an assumption that is built into the definition of the compared systems. Self-citations to Pyserini, RankLLaMA, and Rank-R1 appear only as tooling or related work and are not load-bearing for the main claim. The single-run training limitation noted in Appendix C is a statistical robustness concern, not a circularity, and is acknowledged by the authors. Therefore no circular step is identifiable under the required evidence standard.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard evaluation assumptions (judgment quality, BM25 candidates), on the Rank1 training data and Qwen2.5 family as representatives of reasoning and standard rerankers, and on the methodological premise that a single fine-tuning run is stable. No new entities are introduced. The main free choices are hyperparameters and thresholds, held constant across variants.

free parameters (4)
  • LoRA hyperparameters (rank 32, alpha 64, lr 2e-4, batch size 128, 1 epoch) = fixed across all variants
    Chosen by hand and held constant to enable controlled comparison; they are not tuned to produce the observed gap.
  • ReasonRR-NoReason forced reasoning string = Okay, I have finished thinking.
    Selected following Ma et al. (2025) to disable reasoning; a different placeholder could conceivably alter behavior.
  • Self-consistency sample count = 8 (3 also tested)
    Number of sampled reasoning chains for score averaging; the authors show 3 and 8 samples give similar results.
  • Binary classification threshold for precision/recall = R > 0.5, with relevance judgments > 2 as positive
    Standard threshold and binarization practice in IR, used only for the classification analysis in Section 4.1.
assumptions (4)
  • domain assumption MS MARCO/TREC DL relevance judgments and BRIGHT ground truth are reliable
    Used as gold labels for training and evaluation; any noise is treated as measurement error.
  • domain assumption Qwen2.5 base models fine-tuned with LoRA are representative LLM rerankers
    Limits external validity to the Qwen2.5 family and scale up to 7B; the authors acknowledge this in the Limitations section.
  • domain assumption The Rank1 training data (DeepSeek R1 generated reasoning chains over MS MARCO pairs) is a valid reasoning-reranking training set
    ReasonRR is a reproduction of Rank1, and results depend on the quality and format of this public dataset.
  • domain assumption BM25 top-100 candidate pool is a sufficient evaluation context
    Standard reranking setup; conclusions may not transfer to other candidate distributions or first-stage retrievers.

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Cite this review

Pith. "Pith review of Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?." pith.science (2026). https://pith.science/paper/FPJWHTCQ

@misc{pith2026250516886,
  author       = {Pith},
  title        = {Pith review of: Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FPJWHTCQ}},
  note         = {Machine review of arXiv:2505.16886}
}
read the original abstract

With the growing success of reasoning models across complex natural language tasks, researchers in the Information Retrieval (IR) community have begun exploring how similar reasoning capabilities can be integrated into passage rerankers built on Large Language Models (LLMs). These methods typically employ an LLM to produce an explicit, step-by-step reasoning process before arriving at a final relevance prediction. But, does reasoning actually improve reranking accuracy? In this paper, we dive deeper into this question, studying the impact of the reasoning process by comparing reasoning-based pointwise rerankers (ReasonRR) to standard, non-reasoning pointwise rerankers (StandardRR) under identical training conditions, and observe that StandardRR generally outperforms ReasonRR. Building on this observation, we then study the importance of reasoning to ReasonRR by disabling its reasoning process (ReasonRR-NoReason), and find that ReasonRR-NoReason is surprisingly more effective than ReasonRR. Examining the cause of this result, our findings reveal that reasoning-based rerankers are limited by the LLM's reasoning process, which pushes it toward polarized relevance scores and thus fails to consider the partial relevance of passages, a key factor for the accuracy of pointwise rerankers.

Figures

Figures reproduced from arXiv: 2505.16886 by the authors.

Figure 1
Figure 1. Average NDCG@10 of reasoning pointwise [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Relevance Scores Distribution across Qwen2.5-7B reranker variants on DL19. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Relevance Scores Distribution for ReasonRR [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

33 extracted references · 4 canonical work pages · cited by 1 Pith paper

  1. [1]

    Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016. Ms marco: A human generated machine reading comprehension dataset. arXiv preprint arXiv:1611.09268

    arXiv 2016

  2. [2]

    Voorhees, and Ian Soboroff

    Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Fernando Campos, Jimmy Lin, Ellen M. Voorhees, and Ian Soboroff. 2022. https://api.semanticscholar.org/CorpusID:261302277 Overview of the trec 2022 deep learning track . In Text Retrieval Conference

    work page 2022

  3. [3]

    Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Fernando Campos, and Jimmy J. Lin. 2021 a . https://api.semanticscholar.org/CorpusID:261242374 Overview of the trec 2021 deep learning track . In Text Retrieval Conference

    work page 2021

  4. [4]

    Voorhees

    Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Fernando Campos, and Ellen M. Voorhees. 2020. https://api.semanticscholar.org/CorpusID:253234683 Overview of the trec 2019 deep learning track . ArXiv, abs/2003.07820

    arXiv 2020

  5. [5]

    Voorhees

    Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Fernando Campos, and Ellen M. Voorhees. 2021 b . https://api.semanticscholar.org/CorpusID:212737158 Overview of the trec 2020 deep learning track . ArXiv, abs/2102.07662

    arXiv 2021

  6. [6]

    Rahmani, Daniel Campos, Jimmy Lin, Ellen M

    Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Hossein A. Rahmani, Daniel Campos, Jimmy Lin, Ellen M. Voorhees, and Ian Soboroff. 2023. https://api.semanticscholar.org/CorpusID:274280641 Overview of the trec 2023 deep learning track . In Text Retrieval Conference

    work page 2023

  7. [7]

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948

    arXiv 2025

  8. [8]

    Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. https://openreview.net/forum?id=nZeVKeeFYf9 Lo RA : Low-rank adaptation of large language models . In International Conference on Learning Representations

    2022

Show all 33 references
  1. [9]

    Yifan Ji, Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shi Yu, Yishan Li, Zhiyuan Liu, Yu Gu, Ge Yu, and Maosong Sun. 2025. Learning more effective representations for dense retrieval through deliberate thinking before search. arXiv preprint arXiv:2502.12974

    2025

  2. [10]

    Yu Kang, Xianghui Sun, Liangyu Chen, and Wei Zou. 2025. C3ot: Generating shorter chain-of-thought without compromising effectiveness. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24312--24320

    2025

  3. [11]

    Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 2023. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th Symposium on Operating Systems Princip...

    2023

  4. [12]

    Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A python toolkit for reproducible information retrieval research with sparse and dense representations. In Proceedings of the 44th International ACM SIGIR Conference ...

    2021

  5. [13]

    Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, and Matei Zaharia. 2025. Reasoning models can be effective without thinking. arXiv preprint arXiv:2504.09858

    2025 arXiv

  6. [14]

    Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin. 2024. Fine-tuning llama for multi-stage text retrieval. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2421--2425

    2024

  7. [15]

    Sean MacAvaney, Nicola Tonellotto, and Craig Macdonald. 2022. Adaptive re-ranking with a corpus graph. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 1491--1500

    2022

  8. [16]

    Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, and Se-Young Yun. 2025. Self-training elicits concise reasoning in large language models. arXiv preprint arXiv:2502.20122

    2025 arXiv

  9. [17]

    Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. https://doi.org/10.18653/v1/2020.findings-emnlp.63 Document ranking with a pretrained sequence-to-sequence model . In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 708--718, Onl...

    2020 doi

  10. [18]

    Rulin Shao, Rui Qiao, Varsha Kishore, Niklas Muennighoff, Xi Victoria Lin, Daniela Rus, Bryan Kian Hsiang Low, Sewon Min, Wen-tau Yih, Pang Wei Koh, et al. 2025. Reasonir: Training retrievers for reasoning tasks. arXiv preprint arXiv:2504.20595

    2025 arXiv

  11. [19]

    Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han yu Wang, Liu Haisu, Quan Shi, Zachary S Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan O Arik, Danqi Chen, and Tao Yu. 2025. https://openreview.net/forum?id=ykuc5q381b BRIGHT : A realistic and cha...

    2025

  12. [20]

    Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Hanjie Chen, et al. 2025. Stop overthinking: A survey on efficient reasoning for large language models. arXiv preprint arXiv:2503.16419

    2025 arXiv

  13. [21]

    Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023. Is chatgpt good at search? investigating large language models as re-ranking agents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language...

    2023

  14. [22]

    Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. https://openreview.net/forum?id=1PL1NIMMrw Self-consistency improves chain of thought reasoning in language models . In The Eleventh International Conferenc...

    2023

  15. [23]

    Orion Weller, Kathryn Ricci, Eugene Yang, Andrew Yates, Dawn Lawrie, and Benjamin Van Durme. 2025. Rank1: Test-time compute for reranking in information retrieval. arXiv preprint arXiv:2502.18418

    2025 arXiv

  16. [24]

    Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R \'e mi Louf, Morgan Funtowicz, et al. 2019. Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771

    2019 arXiv

  17. [25]

    Heming Xia, Yongqi Li, Chak Tou Leong, Wenjie Wang, and Wenjie Li. 2025. Tokenskip: Controllable chain-of-thought compression in llms. arXiv preprint arXiv:2502.12067

    2025

  18. [26]

    Ruiran Yan, Zheng Liu, and Defu Lian. 2025. O1 embedder: Let retrievers think before action. arXiv preprint arXiv:2502.07555

    2025 arXiv

  19. [27]

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388

    2025 arXiv

  20. [28]

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. 2024. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115

    2024 arXiv

  21. [29]

    Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov. 2024. Distilling system 2 into system 1. arXiv preprint arXiv:2407.06023

    2024 arXiv

  22. [30]

    Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, and Guido Zuccon. 2025. Rank-r1: Enhancing reasoning in llm-based document rerankers via reinforcement learning. arXiv preprint arXiv:2503.06034

    2025 arXiv

  23. [31]

    Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, and Guido Zuccon. 2024. A setwise approach for effective and highly efficient zero-shot ranking with large language models. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information ...

    2024

  24. [32]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  25. [33]

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

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Reviewed August 7, 2026 · model on record in the stance chip above.