Evaluating Position Bias in Large Language Model Recommendations

Estrid He; Ethan Bito; Yongli Ren

REVIEW 3 major objections 4 minor 3 cited by

Evaluating Position Bias in Large Language Model Recommendations

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper demonstrates position bias in LLM recommendations and shows that RISE, an iterative prompting strategy, reduces it without fine-tuning.

desk verdict Plausible and practically relevant, but with only the abstract readable and no code or data visible, the central claim is unverified; worth peer review if the full text is intact. read the letter →

arxiv 2508.02020 v1 pith:AGL3PBJG submitted 2025-08-04 cs.IR

Ethan Bito , Yongli Ren , Estrid He This is my paper · ORCID

classification cs.IR

keywords positionbiaslargelanguagemodelsrecommendationsystemspromptingstrategyRISErankingstabilityzero-shotinputordersensitivity

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 sets out to show that large language models used for recommendation carry a position bias: changing the order of candidate items in a prompt changes the recommendations, even when the set of candidates is identical. It supports this with experiments on real-world datasets that measure how much outputs move when candidate order is permuted. The paper then claims a prompting strategy called Ranking via Iterative SElection (RISE) reduces that order sensitivity and improves stability across runs. RISE works without fine-tuning the model or adding a post-processing step, so it can be applied to existing instruction-following LLMs directly. If correct, the finding means prompt order should be treated as a variable in any LLM-based recommendation pipeline, and order-insensitive prompting is a practical remedy.

What carries the argument

The central object is RISE (Ranking via Iterative SElection), a prompting protocol that converts one big ranking request into a sequence of smaller selection requests. Instead of asking the LLM to rank a long list at once, it repeatedly asks for the best remaining item, removes the chosen item from the pool, and builds the final ranking one pick at a time. This isolates each decision from the full-order context, so no single ordering of the candidates can dominate the model's output. The method carries the argument because it is the mechanism that is claimed to reduce the measured position sensitivity without any weight updates or output post-processing.

What would settle it

Repeatedly present the same candidate set in many random orders and also many times in the same order; if the variation in recommendations across different orders is no larger than the variation across identical-order repetitions, the claimed position bias is not demonstrated.

Watch

Extended reading notes

Core claim

The central claim is that LLM recommenders are not order-neutral: the same candidate set, listed in different orders, can yield different recommendations, and this sensitivity is systematic rather than random noise. On their benchmark datasets the paper reports that candidate order can disproportionately shape outputs, making recommendation quality unstable. The proposed remedy, RISE, addresses the bias at the prompt level by iteratively asking the model to select the best remaining candidate, removing that item, and repeating until a full ranking is produced. The paper reports that this iterative strategy makes outputs less sensitive to the initial order and more stable across repeated calls, and that it outperforms comparison prompting baselines on key benchmark datasets without model fine-tuning or post-processing.

Load-bearing premise

The paper's conclusions depend on the assumption that output changes observed when only candidate order changes are actually caused by order, and not by random sampling noise, formatting differences, or quirks of the evaluation metric.

Editorial extensions

If this is right

Evaluation of LLM recommenders should include order-sensitivity or stability metrics, not just accuracy on one fixed ordering.
Live recommendation systems can reduce order-driven inconsistencies by applying RISE or shuffling candidates across calls.
RISE can be dropped into any instruction-following LLM without retraining, so its benefit is available immediately to deployed zero-shot recommenders.
Benchmark comparisons among LLM recommenders should control candidate order, since results on a single ordering may reflect position bias rather than true ranking quality.
If RISE stabilizes ranking, repeated-sampling agreement becomes a usable quality signal for LLM recommendation outputs.

Reading between the lines

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

A natural extension beyond this paper is to apply RISE-style iterative selection to other list-generation tasks, such as document reranking or multiple-choice answer aggregation, where the same order-sensitivity pattern may appear.
Because RISE increases the number of LLM calls, a real deployment would trade latency and cost for stability; the paper does not quantify that trade-off.
The bias curves themselves, which positions get favored, could be characterized and compared across model families; a natural follow-up would be examining whether RISE removes the bias or merely averages it out.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

I could not read the full text: the supplied copy is corrupted beyond recovery. So this is a judgment on the abstract plus prior knowledge of the area. The abstract claims LLM recommender outputs change with candidate order, and introduces RISE to reduce that sensitivity without fine-tuning. Position bias in LLMs is an established phenomenon, so the diagnosis is credible. What would be new is RISE as a mitigation plus a clean measurement protocol. The phrase 'reduces sensitivity to input ordering... without requiring model fine-tuning or post-processing' is a concrete, testable promise.

What I cannot verify: dataset names, baselines, metrics, error bars, number of trials, decoding temperature, or code. The stress-test concern about conflating order effects with sampling noise or formatting variation is the right first question for a referee. NDCG@k is position-discounted, so a model that changes confidence but not relative ranking will move the metric; the paper must separate those. None of that is visible here.

The direction is plausible given prior work on LLM position effects, so I would send this to peer review if the actual manuscript is complete and readable. If the only thing that exists is the abstract, then it is not ready, but that is not a fair assumption to make about the authors. The reader's 'unverified' call is correct: it is a lack of information, not a verdict on merit.

For the revision, the authors should be asked to report means and variances, fix or vary temperature deliberately, run each permutation with multiple seeds, and include formatting controls (e.g., numbered list vs. bullet list, no separators). They should also state explicitly how 'sensitivity' is measured and why NDCG movement reflects order sensitivity rather than score noise.

I would cite this if the results hold and the protocol becomes visible; the abstract alone is not enough. It could be a useful reading-group piece about evaluation pitfalls in LLM recommendation. My bottom line: desk reject only if the full text is unavailable or garbled; otherwise send it out.

Referee Report

3 major / 4 minor

Summary. The paper claims that LLM-based recommendation models exhibit position bias: the order of candidate items in a prompt can disproportionately influence model outputs. It proposes a new prompting strategy, Ranking via Iterative SElection (RISE), which is claimed to reduce sensitivity to input ordering and improve stability without fine-tuning or post-processing. The visible evidence consists only of the abstract; the supplied full text is corrupted and appears to belong to a different arXiv submission (2508.02023, cs.SE), so the experimental protocol, datasets, baselines, effect sizes, and statistical details cannot be inspected.

Significance. If the central claim is correct, the paper addresses a practically relevant limitation of LLM-based recommendation: prompt-order invariance is desirable, and a fine-tuning-free mitigation would be cheaply deployable. The contribution is empirical, but the manuscript as supplied offers no reproducible artifacts, no dataset identifiers, no baseline list, and no numerical results; the abstract alone is insufficient to evaluate the strength of the claim. The finding is plausible and would be useful, but it is currently unverified.

major comments (3)

[Abstract (central claim)] The central claim that LLM-based recommendation models suffer from position bias requires an evaluation protocol that isolates item order as the only causal variable. The abstract provides no controls for decoding stochasticity (temperature, random seeds, number of trials), for formatting differences that may arise when permuting items, or for the non-uniform sensitivity of rank-based metrics such as NDCG@k. The supplied full text is corrupted and appears to be from a different arXiv paper, so none of these controls can be checked. This is load-bearing because all three confounds could produce apparent order sensitivity in the absence of true position bias.
[Abstract (RISE claim)] The statement that RISE 'reduces sensitivity to input ordering' is not quantitatively assessable because the sensitivity measure is never defined in the visible text. The authors need to specify the metric (e.g., variance of output rankings across permutations, expected NDCG@k change, or a permutation test statistic) and confirm that RISE and the baselines are compared under identical decoding parameters, prompt templates, and number of trials. Without this, the comparative claim cannot be tested from the supplied material.
[Abstract (empirical findings)] The abstract reports 'systemic biases of LLMs with high sensitivity to input orders' but provides no effect sizes, confidence intervals, dataset names, or baseline results. The phrase 'disproportionately influence' implies a practically meaningful magnitude; the authors should report concrete numbers, such as the average change in NDCG@k or ranking similarity under different permutations, to substantiate that the effect is not merely statistically significant but also practically significant.

minor comments (4)

[Abstract] The phrase 'systemic biases' should likely be 'systematic biases'; please correct the typo.
[Abstract] The terms 'input orders' and 'input ordering' are used interchangeably; choose one term for consistency throughout the abstract and the paper.
[Full text] The supplied full text is corrupted mojibake and appears to contain the header 'arXiv:2508.02023v2 [cs.SE]', not the manuscript for arXiv:2508.02020 (cs.IR); the authors should resubmit a cleanly rendered version of the correct paper so that technical content can be reviewed.
[Metadata] The version date '30 Mar 2026' shown in the full-text header is inconsistent with the arXiv identifier 2508.02020; please verify the versioning and date metadata.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found; the paper's claims are empirical and benchmark-based, and no definitional or self-citation reduction is exhibited.

full rationale

The paper's central claims—that LLM recommendation models exhibit position bias and that the RISE prompting strategy mitigates it—are empirical findings evaluated against real-world datasets and compared with baselines. The abstract states that the authors 'analyse the position bias ... on real-world datasets' and 'compare our proposed method against various baselines on key benchmark datasets.' These claims are supported by external benchmarks and baseline comparisons rather than by definitions or by the paper's own outputs. The full text is corrupted, so no equations, datasets, or implementation details are visible; however, the absence of evidence is not itself circularity. The reviewer's concerns about decoding stochasticity, formatting noise, and metric sensitivity are threats to internal validity or correctness risk, not instances of a prediction reducing to its input by construction. No self-citation is visible, and no fitted parameter is renamed as a prediction. Under the hard rule that circularity must be demonstrated with a specific quote and reduction, no such demonstration is possible from the supplied material. Therefore the appropriate finding is no significant circularity, score 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No free parameters or invented entities are identifiable from the abstract; the analysis rests on domain assumptions about prompt perturbation and benchmark validity.

assumptions (2)

domain assumption Position bias can be isolated by presenting the same candidate set in different orders and observing output changes.
The abstract's analysis of order sensitivity presupposes that order permutations are the only relevant change and that output variation is attributable to position bias.
domain assumption Benchmark datasets and ranking metrics used in the comparisons reflect recommendation quality.
The reported conclusion that RISE improves stability relies on the benchmark evaluation being a valid proxy for recommendation performance.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Evaluating Position Bias in Large Language Model Recommendations." pith.science (2026). https://pith.science/paper/AGL3PBJG

@misc{pith2026250802020,
  author       = {Pith},
  title        = {Pith review of: Evaluating Position Bias in Large Language Model Recommendations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AGL3PBJG}},
  note         = {Machine review of arXiv:2508.02020}
}

read the original abstract

Large Language Models (LLMs) are being increasingly explored as general-purpose tools for recommendation tasks, enabling zero-shot and instruction-following capabilities without the need for task-specific training. While the research community is enthusiastically embracing LLMs, there are important caveats to directly adapting them for recommendation tasks. In this paper, we show that LLM-based recommendation models suffer from position bias, where the order of candidate items in a prompt can disproportionately influence the recommendations produced by LLMs. First, we analyse the position bias of LLM-based recommendations on real-world datasets, where results uncover systemic biases of LLMs with high sensitivity to input orders. Furthermore, we introduce a new prompting strategy to mitigate the position bias of LLM recommendation models called Ranking via Iterative SElection (RISE). We compare our proposed method against various baselines on key benchmark datasets. Experiment results show that our method reduces sensitivity to input ordering and improves stability without requiring model fine-tuning or post-processing.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
cs.IR 2026-07 accept novelty 6.5 of 10

LBR removes length bias in LLM recommenders via length-aware attention offsets and Trie-branching information-length normalization, improving accuracy and fairness with negligible cost.
Voting Biases in Decentralized Autonomous Organization (DAO) Governance
cs.CY 2026-07 conditional novelty 6.0 of 10

Author-selected Snapshot choices show a 58.8 pp higher voting-power share than non-author choices, exceeding approval (27.1 pp) and first-list (7.7 pp) associations.
One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models
cs.CL 2026-02 conditional novelty 6.0 of 10

Reward models still favor long answers, confident-sounding answers, answer positions, sycophancy, and familiar writer styles; nulling linear activation directions fixes the first three but not the last two.

Reference graph

Works this paper leans on

1 extracted references · 1 canonical work pages · cited by 3 Pith papers

[1]

�� ...

work page arXiv 2026

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

�� ...

work page arXiv 2026