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REVIEW 3 major objections 6 minor 28 references

InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Inserting BM25 scores into a listwise prompt makes zero-shot LLM rerankers more effective on reasoning queries.

desk verdict A simple, honestly framed BM25-prompting trick for listwise LLM reranking, with plausible but statistically under-supported gains. read the letter →

arxiv 2506.14086 v1 pith:MOHJLR3V submitted 2025-06-17 cs.IR cs.AIcs.CL

Rahul Seetharaman , Kaustubh D. Dhole , Aman Bansal This is my paper · ORCID
classification cs.IRcs.AIcs.CL
keywords listwisererankingBM25scoreinjectionzero-shotLLMreasoning-centricretrievaleffectivenesspromptengineeringBRIGHTbenchmarkR2MED
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

InsertRank is a test-time change to listwise reranking: instead of feeding an LLM only the query and candidate documents, it also includes the BM25 score each document received from the first-stage retriever. The paper's central claim is that this lexical signal improves retrieval effectiveness on reasoning-centric queries, where simple keyword or semantic matching is not enough. The authors report consistent NDCG@10 gains across GPT, Gemini, and Deepseek model families on two benchmarks, BRIGHT and R2MED, without any fine-tuning and with negligible extra token cost. The best setting, Deepseek-R1 with InsertRank, reaches 37.5 on BRIGHT and 51.1 on R2MED, surpassing previously published results. If right, the result is a low-cost plug-in improvement for any pipeline that already uses BM25 to retrieve candidates and an LLM to rerank them.

What carries the argument

The central object is the score-augmented prompt: each candidate document is listed with its BM25 score, and the documents are ordered from highest to lowest score before the LLM ranks them. This tuple format is what lets the reranker combine lexical evidence with its own reasoning; a single line of instruction tells the model the scores come from a lexical retriever. The ablations show the mechanism is not just "add a number": shuffling the document order removes most of the benefit on R2MED, and rescaling to 0-1 slightly hurts while rescaling to 0-100 preserves the gain, suggesting the LLM reads both relative order and approximate magnitude.

What would settle it

Re-run the vanilla and InsertRank conditions on a sample of BRIGHT and R2MED queries several times per model, varying sampling temperature, and compute confidence intervals for the NDCG@10 difference; if zero frequently falls inside the interval, the claimed improvement is not distinguishable from noise. A second decisive check is replacing the true BM25 scores with permuted or random scores: if the gain persists, the model is not actually using the lexical signal.

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Extended reading notes

Core claim

The discovery is that a reasoning-capable LLM reranker can use the retriever's own BM25 scores as a grounding signal when documents are presented in descending score order. On the authors' terms, injecting the scores anchors the model's step-by-step reasoning to the lexical relevance of the candidate set, countering overthinking and concept drift that can derail long reasoning chains. The paper demonstrates this by adding the BM25 score next to each document in a zero-shot listwise prompt and observing consistent average NDCG@10 improvements over the vanilla listwise prompt across the four model families tested on both BRIGHT and R2MED. Gains in the main tables range from roughly 0.003 to 0.012 in absolute average NDCG@10, and the strongest configuration surpasses the best published baselines on both benchmarks.

Load-bearing premise

The load-bearing premise is that the reported NDCG@10 differences—about 0.003 to 0.012 on average—are larger than the run-to-run noise of the stochastic LLM APIs, since each condition is evaluated once without variance or significance testing.

Editorial extensions

If this is right

  • Any existing BM25-then-LLM reranking pipeline can adopt InsertRank by editing only the prompt, adding no training data and almost no token cost.
  • The consistent gains across GPT, Gemini, and Deepseek suggest the effect transfers across model families, not just one architecture.
  • Because the document order itself matters, a pipeline should keep the first-stage BM25 ordering rather than re-order randomly before reranking, even if no scores are shown.
  • Reasoning-centric retrieval, where overthinking and concept drift are common failure modes, is the setting where score injection helps; applying it to simple keyword queries may not yield the same benefit.

Reading between the lines

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

  • A natural extension is to test whether other cheap first-stage signals—dense similarity scores, hybrid fusion scores, or even rank positions—produce the same grounding effect; if they do, the mechanism is general score injection rather than BM25 specifically.
  • The ablation pattern hints that the LLM uses relative order more than absolute magnitude; a testable prediction is that replacing BM25 values with any strictly decreasing sequence of numbers preserves most of the gain.
  • The R2MED shuffle result suggests the score signal and the order signal interact; an experiment that keeps order but drops scores, or keeps scores but randomizes order, would isolate which component carries the benefit.
  • Because the paper only evaluates reasoning-heavy benchmarks, the boundary of the effect is unknown; testing on standard ad-hoc retrieval collections could show whether the gain is specific to reasoning queries or general.
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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

3 major / 6 minor

Summary. The paper introduces InsertRank, a zero-shot listwise reranking method that injects BM25 scores from a first-stage retrieval into the prompt of an LLM reranker. The method is evaluated on two reasoning-centric retrieval benchmarks, BRIGHT and R2MED, using Gemini 2.0 Flash, Gemini 2.5 Flash, GPT-4o, and Deepseek-R1. The main results (Tables 1 and 2) report average NDCG@10 improvements of 0.003 to 0.012 over vanilla listwise reranking, with the best setting (Deepseek-R1 + InsertRank) reaching 37.5 on BRIGHT and 51.1 on R2MED. The paper also presents ablations on BM25 score normalization and on document-order shuffling. The authors claim this is the first demonstration that injecting retrieval scores into a zero-shot listwise LLM reranker improves reasoning-centric retrieval.

Significance. If the average gains are real, InsertRank is a simple and practically valuable finding: it requires no fine-tuning, adds negligible token overhead, and appears to help across four LLM families and two benchmarks. The method has no fitted parameters, so overfitting to the benchmarks is not a concern. The ablation on document-order shuffling is a useful control, and the normalization experiments give some evidence that the method is not brittle to score scaling. However, the current statistical evidence is too weak to support the strength of the claims, and several numeric inconsistencies in the ablation discussion reduce confidence in the reported details.

major comments (3)
  1. [Section 4, Tables 1-2] The central claim of consistent improvement rests on single runs per condition. The paper reports no variance, confidence intervals, significance tests, or decoding configuration (temperature, top-p, number of runs). The reported differences are between 0.003 and 0.012 NDCG@10; given the benchmark sizes (about 1,300 queries for BRIGHT and 876 for R2MED), a plausible per-query NDCG@10 standard deviation of about 0.3 yields a standard error near 0.008, so the smallest gains are below the noise floor. Please provide repeated-run statistics (for example, paired bootstrap or permutation tests over queries) and state the sampling parameters. Additionally, many individual domains regress (for example, Gemini 2.0 Flash on Stack, Sust, and Pony in BRIGHT; Deepseek-R1 on Psych, Sust, and Pony in BRIGHT; and several R2MED tasks for most models), so the word 'consistent' should be explicitly limited to the average level unless per-domain significance is demonstrated.
  2. [Sections 5.1-5.2, Tables 3-4] The ablation text contains numerical claims that do not match the tables. In Section 5.2, the text says that for BRIGHT, the shuffled setting with BM25 injection gives a 9.4% increase relative to the vanilla setting and a 1.1-point absolute decrease compared with the ordered setting, but Table 4 reports Shuffled w/ BM25 = .322, Vanilla = .334, and ordered w/ BM25 = .345. These values imply a -3.6% relative change and a .023 absolute decrease. In Section 5.1, the reported '0.58% decrease' for the BRIGHT 0-1 normalization is also inconsistent with Table 3, where the 0-1 value (.340) is about 1.45% below the raw-BM25 value (.345). The authors should correct these numbers and state explicitly whether percentages are relative to the vanilla condition, the raw-BM25 condition, or the 0-1 condition.
  3. [Section 4, Tables 1-2] The claim that InsertRank 'surpasses previous methods' compares against published numbers that are not generated under the same protocol. The Deepseek-R1 + InsertRank result uses the GPT-4o CoT query formulation for BRIGHT and the HyDE formulation for R2MED, while the cited baselines (ReasonIR, JudgeRank, Rank-K, Rank1-32B, Rank-R1) use their own retrievers and query processing. The differences involved (for example, .375 vs .369 for ReasonIR on BRIGHT) are small and within the noise range identified above. Please either run the baselines under the same first-stage and query-formulation conditions or soften the 'surpassing' claim to a leaderboard-style comparison with the appropriate caveats.
minor comments (6)
  1. [Abstract and Section 2.1] The abstract says BRIGHT spans '12 diverse domains' while Section 2.1 says '11 domains'; Table 1 lists 12 domain columns. Please reconcile the count.
  2. [Table 4 and Table 7] Table 4's caption 'Effect of shuffling on R2MED' is misleading because the table reports both BRIGHT and R2MED averages; Table 7's caption should also indicate that it reports BRIGHT results.
  3. [Section 3 and Appendix 7.4] The notation for BM25 scores is inconsistent: equation (4) uses b_i, the prompt template in Section 3 uses s_1 and then b_n, and the appendix prompt uses 'BM25 score: {score_1}'. Please use one notation throughout.
  4. [Sections 5.1-5.2] The prose says 'as evidenced in 3' and 'as evidenced in 4' rather than 'Table 3' and 'Table 4'.
  5. [Appendix 7.3.4] The 'Gold Answer' in Appendix 7.3.4 is about drug overdose, which does not match the bedtools/Ubuntu query used in the reasoning traces in Sections 7.3.1-7.3.3; please replace it with the correct gold answer.
  6. [Section 4] The paper does not state whether the reported NDCG@10 values are computed with the official benchmark scripts; please state the metric and the evaluation setup explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: InsertRank uses BM25 scores as an external input feature, and the reranked output is not equivalent to the input by construction.

full rationale

The paper's central claim is an empirical comparison between listwise LLM reranking with and without BM25 scores injected into the prompt, evaluated against fixed external benchmark labels (BRIGHT and R2MED). BM25 scores are computed by an external first-stage retriever and are not fitted to the evaluation labels; no model parameters are trained or tuned, and the reranked list is generated by the LLM rather than derived algebraically from the input scores. The shuffle ablation (Tables 4-6) directly tests whether the LLM simply copies the input ordering and shows that shuffled order with BM25 scores changes performance, indicating the score signal is not merely re-emitting the input order. The paper's two self-citations ([5], [6]) appear only in related-work context and do not carry the load-bearing argument. The primary weakness of the paper is statistical: reported gains are small and no variance or significance tests are provided, but that is an epistemic gap in an empirical claim, not a circular derivation.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No parameters are fitted to the benchmark labels. BM25 parameters and the token limit are chosen from standard practice or prior work; the normalization scale (0-100) is tested as an ablation rather than fitted. The paper introduces no new particles, forces, dimensions, or theoretical entities; it only modifies the prompt content.

assumptions (3)
  • domain assumption BM25 scores are a useful lexical relevance signal for LLM reranking.
    The entire method depends on scores conveying meaningful relevance information; the paper tests this empirically but does not prove it.
  • domain assumption Single-sample LLM API outputs adequately estimate ranking quality.
    Results are reported as one NDCG@10 value per condition with no repeated sampling or significance testing.
  • domain assumption The chosen query reformulations (GPT-4o CoT for BRIGHT, HyDE for R2MED) are the appropriate shared input across compared methods.
    These reformulations are taken from prior benchmark analyses; they are not varied in the comparison.

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Cite this review

Pith. "Pith review of InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking." pith.science (2026). https://pith.science/paper/MOHJLR3V

@misc{pith2026250614086,
  author       = {Pith},
  title        = {Pith review of: InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MOHJLR3V}},
  note         = {Machine review of arXiv:2506.14086}
}
read the original abstract

Large Language Models (LLMs) have demonstrated significant strides across various information retrieval tasks, particularly as rerankers, owing to their strong generalization and knowledge-transfer capabilities acquired from extensive pretraining. In parallel, the rise of LLM-based chat interfaces has raised user expectations, encouraging users to pose more complex queries that necessitate retrieval by ``reasoning'' over documents rather than through simple keyword matching or semantic similarity. While some recent efforts have exploited reasoning abilities of LLMs for reranking such queries, considerable potential for improvement remains. In that regards, we introduce InsertRank, an LLM-based reranker that leverages lexical signals like BM25 scores during reranking to further improve retrieval performance. InsertRank demonstrates improved retrieval effectiveness on -- BRIGHT, a reasoning benchmark spanning 12 diverse domains, and R2MED, a specialized medical reasoning retrieval benchmark spanning 8 different tasks. We conduct an exhaustive evaluation and several ablation studies and demonstrate that InsertRank consistently improves retrieval effectiveness across multiple families of LLMs, including GPT, Gemini, and Deepseek models. %In addition, we also conduct ablation studies on normalization by varying the scale of the BM25 scores, and positional bias by shuffling the order of the documents. With Deepseek-R1, InsertRank achieves a score of 37.5 on the BRIGHT benchmark. and 51.1 on the R2MED benchmark, surpassing previous methods.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 15, 2026 · model on record in the stance chip above.