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arxiv: 2604.09553 · v1 · submitted 2026-01-31 · 💻 cs.IR · cs.AI

SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Models

Jianhong Li , Zeheng Qian , Wangze Ni , Haoyang Li , Hongwei Yao , Yang Bai , Kui Ren This is my paper

Pith reviewed 2026-05-16 09:17 UTC · model grok-4.3

classification 💻 cs.IR cs.AI
keywords sequential recommendationlarge language modelsbenchmarkfairnessprompt engineeringevaluationstabilityefficiency
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The pith

SRBench supplies a multi-dimensional benchmark that fairly compares neural and LLM-based sequential recommendation models.

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

Existing SR benchmarks overemphasize accuracy, fail to support LLM models through suitable inputs, and lack reliable ways to parse their unstructured answers. SRBench fixes these gaps with a framework that measures accuracy alongside fairness, stability, and efficiency; a single prompt-engineering input format that levels the field for LLM-SR and NN-SR models; and a prompt-extractor mechanism that forces formatted outputs and pulls numeric answers cleanly. When applied to thirteen models, the benchmark surfaces concrete patterns such as LLM-based recommenders overweighting item popularity. The result is a shared evaluation standard that can steer both research and deployment choices.

Core claim

SRBench introduces three coordinated designs: a multi-dimensional evaluation covering accuracy, fairness, stability and efficiency; a unified prompt-engineering input that improves LLM-SR performance and permits head-to-head comparison with neural models; and a prompt-extractor-coupled mechanism that enforces output format via prompts and then applies a numeric-oriented extractor to retrieve answers from free-form LLM text. Evaluations of thirteen mainstream models under this regime yield observations including the tendency of LLM-SR models to over-focus on popularity while under-attending to item quality.

What carries the argument

The prompt-extractor-coupled extraction mechanism, which pairs prompt-enforced output formatting with a numeric-oriented extractor to pull task-specific answers from unstructured LLM responses.

If this is right

  • Direct, apples-to-apples comparison of NN-SR and LLM-SR models becomes feasible under identical input and output conditions.
  • Evaluations now include fairness, stability, and efficiency, revealing trade-offs that accuracy-only benchmarks miss.
  • LLM-SR models are shown to over-weight popularity and under-weight quality, guiding targeted improvements.
  • A reproducible extraction pipeline supports consistent results across different LLM back-ends and prompt styles.

Where Pith is reading between the lines

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

  • The same prompt-plus-extractor pattern could be adapted to create comparable benchmarks for non-recommendation LLM tasks that require numeric or ranked outputs.
  • If the multi-dimensional scores reorder models differently from accuracy alone, practitioners may need to re-weight metrics for specific deployment constraints.
  • Extending the benchmark to additional datasets or newer LLM architectures would test whether the observed popularity bias persists.

Load-bearing premise

The unified prompt input format and prompt-extractor mechanism release LLM capability without introducing new biases or artifacts that favor one model family over another.

What would settle it

Re-running the thirteen-model comparison with an independent answer-parsing method that produces materially different rankings or fairness scores would show the extraction step distorts results.

Figures

Figures reproduced from arXiv: 2604.09553 by Haoyang Li, Hongwei Yao, Jianhong Li, Kui Ren, Wangze Ni, Yang Bai, Zeheng Qian.

Figure 1
Figure 1. Figure 1: Frameworks for Sequential Recommendation: (a) NN-SR methods and (b) LLM-SR methods. LLMs’ inherent randomness, LLMs’ outputs may con￾tain hallucinations, which makes it harder to accurately extract answers for validation. In this paper, we propose SRBench to address the aforemen￾tioned challenges and make the contributions: • To overcome Challenge 1, we conduct a comprehensive in-depth survey to analyze re… view at source ↗
Figure 2
Figure 2. Figure 2: Process of Content Extraction in SRBench. required for LLM-SR process. Specifically, according to the research on LLM reasoning (Ke et al., 2025) and prompting strategy (Lu et al., 2024), we extend SR objective with role￾playing instructions, which proactively activates LLM’s specialized knowledge storage in fields of SR. As per the item IDs in user interaction sequence, we additionally attach related deta… view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance Comparison between Few-shot and Full-length SR Process. (Percentage represents the difference that few-shot compares to full-length SR process in each metric) Yelp dataset and Grok-3 on all datasets. To address this issue, additional validation like item ID verification is required when users are directly recommended by LLM-SR models. • Impact of LLM Thinking Mode. Although LLM￾SR model’s extra… view at source ↗
Figure 6
Figure 6. Figure 6: Overall Performance Comparison on Yelp Dataset: (a) NN-SR models and (b) LLM-SR models. demonstrates suitability for time-sensitive deployment. In contrast, LLM-SR models shows higher computational costs, particularly Grok-3 and Claude-Sonnet-4-thinking, which constrains their practical use in real-time recommendation settings. While achieving higher accuracy, LLM-SR models may exhibit disproportionately h… view at source ↗
Figure 7
Figure 7. Figure 7: Overall Performance Comparison on ML-100K Dataset: (a) NN-SR models and (b) LLM-SR models. substantially higher hallucination rates under few-shot settings, reaching up to 16.58% on the Beauty dataset and 17.52% on Yelp. These results highlight a clear divergence between open-source LLMs and commercial or larger-scale models in terms of output reliability. While certain online LLMs achieve both high recomm… view at source ↗
Figure 8
Figure 8. Figure 8: Overall Performance Comparison on Beauty Dataset: (a) NN-SR models and (b) LLM-SR models. is not inherently guaranteed under LLM-SR paradigms. In addition, substantially longer inference times (ART) are suffered by LLM-SR models, which makes their deployment less practical in latency-constrained environments. Therefore, these observations mean that LLM-SR models benefit from richer semantic knowledge and c… view at source ↗
read the original abstract

LLM development has aroused great interest in Sequential Recommendation (SR) applications. However, comprehensive evaluation of SR models remains lacking due to the limitations of the existing benchmarks: 1) an overemphasis on accuracy, ignoring other real-world demands (e.g., fairness); 2) existing datasets fail to unleash LLMs' potential, leading to unfair comparison between Neural-Network-based SR (NN-SR) models and LLM-based SR (LLM-SR) models; and 3) no reliable mechanism for extracting task-specific answers from unstructured LLM outputs. To address these limitations, we propose SRBench, a comprehensive SR benchmark with three core designs: 1) a multi-dimensional framework covering accuracy, fairness, stability and efficiency, aligned with practical demands; 2) a unified input paradigm via prompt engineering to boost LLM-SR performance and enable fair comparisons between models; 3) a novel prompt-extractor-coupled extraction mechanism, which captures answers from LLM outputs through prompt-enforced output formatting and a numeric-oriented extractor. We have used SRBench to evaluate 13 mainstream models and discovered some meaningful insights (e.g., LLM-SR models overfocus on item popularity but lack deep understanding of item quality). Concisely, SRBench enables fair and comprehensive assessments for SR models, underpinning future research and practical application.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The paper proposes SRBench, a benchmark for sequential recommendation (SR) models that incorporates large language models (LLMs). It identifies limitations in prior benchmarks—an overemphasis on accuracy, datasets that hinder fair LLM vs. neural-network comparisons, and unreliable extraction from LLM outputs—and addresses them via three designs: a multi-dimensional evaluation framework (accuracy, fairness, stability, efficiency), a unified prompt-engineering input paradigm, and a prompt-extractor-coupled mechanism that enforces output formatting plus a numeric-oriented extractor. The authors evaluate 13 models and report insights such as LLM-SR models overfocusing on item popularity while lacking deep item-quality understanding.

Significance. If the extraction mechanism and unified paradigm prove robust, SRBench could standardize multi-faceted SR evaluation, enabling fairer comparisons across model families and directing attention to practical concerns such as stability and fairness. This would support more reliable progress in LLM-based recommendation research and deployment.

major comments (3)
  1. [§3.3] §3.3 (prompt-extractor-coupled mechanism): the central fairness claim depends on reliable, unbiased extraction, yet no extraction success rates, failure analysis, or adherence statistics are reported across the 13 models; variable instruction-following could systematically favor aligned models and reintroduce the bias the benchmark aims to eliminate.
  2. [§4] §4 (evaluation): the reported insights (e.g., LLM-SR popularity focus) lack statistical significance tests, run-to-run variance, or prompt-sensitivity controls, undermining the claim that SRBench enables reproducible and fair assessments.
  3. [§3.2] §3.2 (unified input paradigm): without ablation results showing performance lift and fairness gains for both NN-SR and LLM-SR families, the assertion that prompt engineering produces unbiased comparisons remains unverified.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'some meaningful insights' should be replaced by a concrete example to improve clarity.
  2. [§4] Dataset description: provide explicit statistics (size, sparsity, temporal splits) for all datasets used in §4 to allow replication.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments on SRBench. We address each major comment below and will incorporate revisions to strengthen the manuscript's claims regarding fairness, reproducibility, and the unified paradigm.

read point-by-point responses

  1. Referee: [§3.3] §3.3 (prompt-extractor-coupled mechanism): the central fairness claim depends on reliable, unbiased extraction, yet no extraction success rates, failure analysis, or adherence statistics are reported across the 13 models; variable instruction-following could systematically favor aligned models and reintroduce the bias the benchmark aims to eliminate.

    Authors: We agree that explicit reporting of extraction success rates, failure analysis, and adherence statistics would better substantiate the fairness claim. The prompt-extractor-coupled mechanism relies on strict prompt-enforced output formatting combined with a numeric-oriented extractor to standardize parsing across models. In the revised manuscript, we will add a dedicated analysis (new table and subsection in §3.3) reporting per-model extraction success rates, categorized failure cases (e.g., format violations vs. content errors), and adherence statistics. This will allow readers to assess whether variable instruction-following introduces systematic bias. revision: yes

  2. Referee: [§4] §4 (evaluation): the reported insights (e.g., LLM-SR popularity focus) lack statistical significance tests, run-to-run variance, or prompt-sensitivity controls, undermining the claim that SRBench enables reproducible and fair assessments.

    Authors: The insights were derived from the primary evaluation configuration. To improve reproducibility, the revised version will include: (i) multiple runs with different random seeds to report run-to-run variance and standard deviations for key metrics; (ii) statistical significance tests (e.g., paired t-tests) for model comparisons; and (iii) prompt-sensitivity controls by evaluating a subset of models under varied prompt phrasings. These additions will be integrated into §4 and the experimental setup. revision: yes

  3. Referee: [§3.2] §3.2 (unified input paradigm): without ablation results showing performance lift and fairness gains for both NN-SR and LLM-SR families, the assertion that prompt engineering produces unbiased comparisons remains unverified.

    Authors: The unified input paradigm standardizes data representation to enable direct comparison. While the main results demonstrate competitive LLM-SR performance, we acknowledge the value of explicit ablations. In the revision, we will add ablation studies in §3.2 (and cross-referenced in §4) that evaluate both NN-SR and LLM-SR families with and without the prompt-engineering components, quantifying lifts in accuracy, fairness, stability, and efficiency metrics to verify the gains and fairness benefits. revision: yes

Circularity Check

0 steps flagged

No circularity in benchmark proposal

full rationale

The paper introduces SRBench as an empirical benchmark with three design elements (multi-dimensional metrics, unified prompt paradigm, and prompt-extractor mechanism) and reports evaluations of 13 existing models. No mathematical derivations, first-principles predictions, fitted parameters repurposed as outputs, or self-referential uniqueness theorems appear in the provided text. Claims rest on the new benchmark's construction and observed empirical results rather than any reduction of outputs to inputs by definition or self-citation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the premise that existing SR benchmarks are limited in the three stated ways and that the new designs overcome them; no free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption Existing benchmarks overemphasize accuracy, use datasets that disadvantage LLMs, and lack reliable extraction from unstructured outputs
    Stated directly in the abstract as motivation without supporting citations or data in the provided text.
invented entities (1)
  • prompt-extractor-coupled extraction mechanism no independent evidence
    purpose: To capture task-specific answers from unstructured LLM outputs via prompt-enforced formatting and numeric extraction
    New mechanism proposed to solve the third limitation; no independent evidence outside the paper is provided in the abstract.

pith-pipeline@v0.9.0 · 5548 in / 1375 out tokens · 74381 ms · 2026-05-16T09:17:39.521629+00:00 · methodology

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Reference graph

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