Pith. sign in

REVIEW 2 major objections 7 minor 3 cited by

DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation

T0 review · 2 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read DynamicRAG lets one reranker decide how many documents each query needs

desk verdict A plausible training recipe for dynamic RAG reranking, but the SOTA claim is contradicted by the paper's own table and the evaluation protocol is unfair. read the letter →

arxiv 2505.07233 v2 pith:BZ57F6XY submitted 2025-05-12 cs.CL cs.AI

Jiashuo Sun , Xianrui Zhong , Sizhe Zhou , Jiawei Han This is my paper · ORCID
classification cs.CLcs.AI
keywords dynamicrerankingretrieval-augmentedgenerationreinforcementlearningdirectpreferenceoptimizationknowledge-intensivequestionansweringadaptivedocumentselectionLLMfeedbackRAGefficiency
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 proposes DynamicRAG, a retrieval-augmented generation framework in which a single fine-tuned language model acts as both reranker and generator. Instead of using a fixed number of retrieved documents for every query, the reranker is trained to dynamically choose both the order and the number of documents to pass to the generator. The training signal is the quality of the generated answer itself, turned into a reward through reinforcement learning. If the central claim is right, RAG systems can adapt their context size per question while needing only two LLM calls at inference, and they can do so with substantially less training data than comparable fixed-k approaches.

What carries the argument

The key machinery is the reranker-as-agent with an environment reward. The agent is prompted to generate document identifiers one at a time and to stop once the selected set is sufficient, so the action is an ordered subsequence of variable length. The generator is treated as the environment; after the reranker selects documents, the generator produces an answer, and a weighted combination of Exact Match, Semantic Similarity (BERTScore), Textual Fluency (ROUGE), Length Penalty, and an LLM-based evaluation score becomes the reward. Behavior cloning gives the policy a cold start from expert demonstrations, and DPO then reinforces trajectories that led to higher rewards over those that led to lower rewards, so the model learns not only which documents are relevant but how many to include per query.

What would settle it

Run DynamicRAG with the same top-10 candidate pool that the baselines receive instead of its default top-45. If its Exact Match on NQ, HotpotQA, and ASQA drops to baseline levels, the method's advantage comes from the wider candidate pool rather than from learning to choose the number of documents k. A second check is to measure retrieval recall at k=10 after reranking on the top-10 pool and compare it against the recall reported for the top-45 pool.

Watch

Extended reading notes

Core claim

The central claim is that a RAG reranker can be optimized as a reinforcement-learning agent whose reward is the downstream generation quality, and that this yields both higher accuracy and better efficiency than static Top-K reranking. The reranker produces a list-wise selection: it outputs a reordered subset of the candidate documents, stopping when it judges enough information has been collected. Training proceeds in two stages: behavior cloning from an expert reranker (MonoT5) to learn the action space, then Direct Preference Optimization (DPO) on trajectory pairs ranked by a multi-dimensional reward combining Exact Match, BERTScore, ROUGE, a length penalty, and an LLM-based judge. Across seven knowledge-intensive datasets (NQ, TriviaQA, HotpotQA, 2WikimQA, ASQA, FEVER, ELI5), DynamicRAG reports state-of-the-art results among models of the same parameter size, surpassing strong baselines such as RankRAG and ChatQA-1.5 while using roughly a third of their training data.

Load-bearing premise

The reported state-of-the-art results assume that comparing a reranker that receives the top 45 retrieved documents against baselines that receive only the top 10 is fair; if the extra candidate documents are what produce the gains, the central claim of dynamic selection would not be established.

Editorial extensions

If this is right

  • If DynamicRAG's results hold, a single fine-tuned LLM can serve as both reranker and generator, reducing the pipeline to two LLM calls per query: one to select documents and one to generate the answer.
  • Dynamic selection of k means the context window adapts to query difficulty, which should reduce both the missing-information problem of too-small k and the noise-from-irrelevant-documents problem of too-large k.
  • The reward-from-generation principle suggests that any task with an automatic quality signal could train its reranker without extra human labels, potentially generalizing beyond the seven datasets tested.
  • Training with roughly 150k examples (and only 20k for the cold-start reranker) offers a data-efficiency story relative to approaches that require 400k-500k examples for similar or worse performance.
  • Because inference adds only a small latency overhead over vanilla RAG (about 2.3x in their measured setup) while improving accuracy, the method could make RAG pipelines both more accurate and more economical in practice.

Reading between the lines

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

  • The paper's headline comparison gives DynamicRAG the top 45 retrieved documents from Contriever-MS MARCO but gives all baselines only the top 10; a natural follow-up is to feed DynamicRAG the same top-10 pool to isolate whether the gains come from dynamic selection or simply from a larger candidate set.
  • The same reward-from-generation recipe could be applied to other structured pipelines beyond QA, such as summarization, open-domain slot filling, or tool-use agents, whenever an automatic reward can be computed; the paper does not test these settings.
  • The observed shift of the chosen k distribution after RL (from mostly 14-15 toward 12-14 under a length penalty) suggests the reward design directly controls the efficiency-accuracy trade-off; varying the length-penalty weight could let practitioners dial in a desired operating point.
  • Since the paper reports that closed-source GPT-4o also improves when paired with the dynamic reranker, the selection mechanism appears to be complementary to the generator's own strength, pointing toward a plug-in reranker that could be trained once and applied to many generators.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 7 minor

Summary. This manuscript introduces DynamicRAG, a RAG framework in which a trainable LLM-based reranker decides both the ordering of the retrieved documents and the number k of documents passed to the generator. The reranker is trained in two stages: behavior cloning from monoT5 expert trajectories, followed by DPO-style reinforcement learning in which rewards are computed from the generator's output quality (exact match, BERTScore, ROUGE, a length penalty, and an LLM-based score) against ground-truth answers. At inference the system requires two LLM calls per query, and a single fine-tuned model can serve as both reranker and generator. Experiments are reported on seven knowledge-intensive benchmarks (NQ, TriviaQA, HotpotQA, 2WikimQA, ASQA, FEVER, ELI5) with LLaMA2-7B, LLaMA2-13B, and LLaMA3-8B backbones, together with ablations on the RL stage, reward components, retriever choice, and training-data scale. The paper claims state-of-the-art results among models of the same parameter size while using substantially less training data than the strongest baselines.

Significance. The core idea—treating k as a per-query decision variable and optimizing it with feedback derived from downstream generation quality—is sensible and relatively underexplored, and the paper provides credible internal evidence that the mechanism works: removing the RL stage degrades NQ EM from 48.4 to 44.6 (Table 3b); removing reranking costs roughly 12 EM points on NQ; the reward-weight configuration is stable under perturbation (Appendix B.2, Table 6); and the reranker reaches competitive recall with about 20k training samples versus about 50k for RankRAG (Table 2). I also find no circularity in the training objective: the reward in Eq. (15) is computed against external ground truth using standard metrics. The authors additionally release code, data, and qualitative examples. However, the headline SOTA claim is contradicted by the paper's own Table 1, and the comparison protocol is confounded (Major Comments 1-2); the contribution is promising but substantially overstated in its present form.

major comments (2)
  1. [Abstract; §4.4.1; Table 1] The abstract's claim of "state-of-the-art results among models of same parameter sizes" and §4.4.1's statement that DynamicRAG achieves "superior performance compared to other retrieval-based models such as RankRAG and ChatQA-1.5" are directly contradicted by the paper's own Table 1. For the same LLaMA3-8B backbone, RankRAG outperforms DynamicRAG on NQ (50.6 vs 48.4 EM), TriviaQA (82.9 vs 78.3 EM; 89.5 vs 87.4 accuracy), and FEVER (93.8 vs 91.4 accuracy), while DynamicRAG wins on HotpotQA (36.7 vs 35.3) and 2WikimQA (34.2 vs 31.4), and ASQA is not reported for RankRAG. Because this SOTA assertion is the paper's central claim, it must be re-scoped to explicit per-dataset comparisons, and the "superior performance" wording in §4.4.1 should be replaced with an accurate summary, e.g., that DynamicRAG is competitive with RankRAG while using roughly one-third of its training data and wins on the multi-hop datasets.
  2. [§4.3; Appendix B.1; Table 1] The headline comparison is confounded by candidate-pool size. Section 4.3 states: "By default, we use the top 45 documents from Contriever-MS MARCO as input to the reranker. In contrast, all baseline methods use the top 10 documents from Contriever-MS MARCO as input to ensure a fair comparison." This protocol does not control the candidate-pool variable, so the reported gains cannot be attributed to dynamic k-selection rather than to the larger pool available to DynamicRAG; the sentence's claim to fairness is the opposite of the design. The issue is material because Appendix B.1 (Figure 7) shows that reranker performance varies with the number of input candidates. The authors should add a controlled comparison, such as DynamicRAG with top-10 candidates and baselines with top-45 candidates where context windows allow, and should additionally clarify whether the Table 1 baseline numbers were re-run with a shared retriever or transcribed from the original papers (the caption "Results are directly from the original paper" suggests the latter, which adds a retriever discrepancy to the candidate-pool discrepancy). The cross-backbone rows are likewise not directly comparable given that LLaMA2 models receive top-20 documents while LLaMA3 receives top-45.
minor comments (7)
  1. [Algorithm 1; §3.2.2] Algorithm 1 (line 13) writes the DPO objective without the reference-policy ratio that appears in Eqs. (13)-(14) of §3.2.2; the algorithm should match the main-text objective or define the notation explicitly.
  2. [Appendix B.2; Table 6] In Appendix B.2, both alternative reward configurations in Table 6 are labeled "Hyperparameter 1"; the second and third should be numbered 2 and 3.
  3. [Appendix C.1; §3.2.2] The value of the DPO coefficient β in Eq. (13) is never reported in the training configuration given in Appendix C.1.
  4. [§4.3] Section 4.3 states that temperature 0.2 is used "to enhance output diversity," but a temperature below 1 reduces sampling diversity; the phrasing should be corrected.
  5. [§4.6.1; Table 4] Section 4.6.1 and Table 4: the text claims a "2.3× latency increase" over vanilla RAG, but the table's numbers (Question + Top-10 at 0.57 seconds vs Question + Top-k at 0.61 seconds) imply roughly 1.07×; moreover, the "17×" throughput figure rests on a self-constructed "functional equivalent" of RankRAG whose fidelity to the original implementation is not established, so the claim should be stated more cautiously.
  6. [Table 1 caption] In Table 1, the column labeled "EM/Acc" mixes metrics across datasets, and the caption notes that baseline results are "directly from the original paper"; a footnote listing each baseline's retriever, corpus, and evaluation script would help readers assess comparability.
  7. [§4.6.2; Table 4] Section 4.6.2 reports that k is capped at 15 during training and sampling, while Table 4 reports an average k of 12 at inference; a sentence explaining the relationship between these two settings would remove the apparent tension.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the RL/DPO reward chain is grounded in external ground-truth metrics and standard benchmarks.

full rationale

The derivation chain is self-contained and non-circular. Behavioral cloning uses external expert trajectories from MonoT5 (Section 3.2.1, Eq. 10); generator SFT uses standard supervised likelihood with ground-truth answers; the RL stage (Section 3.2.2) samples trajectories and pairs them by a reward (Eq. 15) that is computed from EM, BERTScore, ROUGE, a length penalty, and LLMScore, all evaluated against the ground-truth answer y_gt. No equation or fitted parameter reduces to the paper's SOTA claim, and no load-bearing claim rests on a self-citation: the cited sliding-window technique (Appendix B.1, 'the same technique as Sun et al. [51]') is from a different author group and is not load-bearing. The training loop does use the model's own generator outputs as reward signals, but that is the intended RL credit-assignment mechanism, not definitional circularity. Concerns raised by the paper itself, such as the top-45 candidate pool for DynamicRAG versus top-10 for baselines (Section 4.3) and the internal mismatch between Table 1 numbers and the Section 4.4.1 statement of 'superior performance compared to...RankRAG,' are experimental-fairness and correctness issues; they do not constitute circular reasoning. Therefore the circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several hand-set parameters (reward weights, expert threshold, k cap, candidate count) and on domain assumptions that link answer-quality metrics to document relevance. No new physical or mathematical entities are introduced. The heaviest burden is the candidate-count asymmetry, which is a design choice rather than a fitted constant.

free parameters (5)
  • Reward weights (alpha, beta, gamma, lambda, delta) = (0.2, 0.2, 0.2, 0.2, 0.2)
    Hand-chosen weights for the reward combination in Eq. 15. Robustness tests in Appendix B.2 show low sensitivity, so this is a mild parameter burden.
  • MonoT5 expert threshold tau = 0.8
    Threshold on expert relevance scores used to construct behavior-cloning trajectories (Appendix C.1). Hand-set and not motivated by data.
  • Maximum selected documents cap = 15
    Upper bound on k for reranker output, set for fairness with top-10 baselines (Section 4.6.2 and Appendix C.1).
  • Top-N candidate count = 45 (LLaMA3), 20 (LLaMA2)
    Number of documents fed to the reranker. Asymmetry with baselines' top-10 is a confound for the main comparison.
  • DPO beta = not reported
    Inverse temperature in the DPO loss (Eq. 13) is not specified, leaving the exact optimization obscure.
assumptions (5)
  • domain assumption Generator response quality metrics (EM, BERTScore, ROUGE, length, LLM score) are valid rewards for document selection.
    The entire RL stage in Sec. 3.2.3 optimizes this reward; if the metrics do not reflect document relevance, the learned k is meaningless.
  • domain assumption The generator's answer quality is a monotonic proxy for the relevance of the selected documents.
    Sec. 3.1 claims that feedback from output quality guides reranking; this assumes a usable signal-to-noise ratio in the reward.
  • standard math DPO optimizes the expected reward objective (Eq. 11) as in Rafailov et al.
    Eqs. 13-14 invoke DPO and rely on the known equivalence to reward optimization without re-deriving it.
  • domain assumption MonoT5 scores, thresholded at tau, define expert-optimal document subsets.
    Behavior cloning in Sec. 3.2.1 and Appendix C.1 treats MonoT5 as the expert; the threshold is arbitrary.
  • domain assumption Training on the train splits of the evaluation benchmarks does not leak into test evaluation.
    Appendix C.1.2 lists training data from NQ, HotpotQA, ELI5, TriviaQA, FEVER, and ASQA; standard train/test splits are assumed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation." pith.science (2026). https://pith.science/paper/BZ57F6XY

@misc{pith2026250507233,
  author       = {Pith},
  title        = {Pith review of: DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BZ57F6XY}},
  note         = {Machine review of arXiv:2505.07233}
}
abstract

Retrieval-augmented generation (RAG) systems combine large language models (LLMs) with external knowledge retrieval, making them highly effective for knowledge-intensive tasks. A crucial but often under-explored component of these systems is the reranker. Since irrelevant documents in RAG systems can mislead the generator, the reranker plays a vital role in refining retrieved documents to enhance generation quality and explainability. However, it is challenging to determine the appropriate number of documents ($k$) that the reranker should select: too few may result in missing critical information, while too many introduce noise and inefficiencies. Although recent studies have explored LLM-based rerankers, they primarily leverage internal model knowledge and overlook the rich supervisory signals that LLMs can provide, such as using response quality as feedback for optimizing reranking decisions. In this paper, we propose DynamicRAG, a novel RAG framework where the reranker dynamically adjusts both the order and number of retrieved documents based on the query. We model the reranker as an agent optimized through reinforcement learning (RL), using rewards derived from LLM output quality. Across seven knowledge-intensive datasets, DynamicRAG demonstrates superior performance, achieving state-of-the-art results among models of same parameter sizes. The model, data and code are available at https://github.com/GasolSun36/DynamicRAG.

Figures

Figures reproduced from arXiv: 2505.07233 by the authors.

Figure 1
Figure 1. Illustration of our dynamic reranker framework. (a) It represents a RAG system with￾out a reranker, where the system primarily focuses on training LLMs. (b) It represents a RAG sys￾tem with a reranker, where the reranker is trained to filter the Top-N documents to a fixed Top-K, which remains constant for all queries. (c) In con￾trast, it represents our dynamic reranker, where both the reranker and the generator are… view at source ↗
Figure 2
Figure 2. Illustration of the training paradigm for [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Illustration of our training framework. During the training phase, we have a total of six [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Performance with different retrievers between Vanilla-RAG and DynamicRAG. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Comparison of different RAG models in terms of efficiency and effectiveness. The x-axis represents the number of LLM calls, while the y￾axis denotes the average performance on the NQ benchmark. Models closer to the top-left corner achieve better overall performance. Fr…
Figure 6
Figure 6. Figure 6: Distribution of reranked document num￾bers (k) on NQ and HotpotQA before and after RL training. k is truncated at 15 to ensure a fair com￾parison, as we restrict k ≤ 15 during both training and sampling. We analyzed the reranked results of Dynami￾cRAG on NQ and HotpotQ…
Figure 7
Figure 7. Figure 7: The impact of varying the number of Top- [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]

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

  1. Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

    cs.CL 2026-04 conditional novelty 6.0 of 10

    RRPO formulates document reranking as a sequential MDP and optimizes a pointwise reranker with PPO using LLM generation rewards and a reference-anchored deterministic baseline.

  2. RECON: Reasoning with Condensation for Efficient Retrieval-Augmented Generation

    cs.CL 2025-10 conditional novelty 5.0 of 10

    Placing a frozen, distilled summarizer between search and reasoning improves RL-RAG exact match (up to 14.5% relative on a 3B agent) while cutting context length by 35%.

  3. Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

    cs.SE 2024-11 conditional novelty 5.0 of 10

    RAG deployment must be context-aware: QA benefits from 5 to 10 retrieved documents, code generation has no stable optimal document count, and prompting helps code tasks far more than QA tasks.

Reference graph

Works this paper leans on

83 extracted references · 44 canonical work pages · cited by 3 Pith papers

  1. [1]

    A. Asai, Z. Wu, Y . Wang, A. Sil, and H. Hajishirzi. Self-rag: Learning to retrieve, generate, and critique through self-reflection, 2023. URL https://arxiv.org/abs/2310.11511

    arXiv 2023

  2. [2]

    Bevilacqua, G

    M. Bevilacqua, G. Ottaviano, P. S. H. Lewis, S. Yih, S. Riedel, and F. Petroni. Autoregressive search engines: Generating substrings as document identifiers. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Process- ing Systems 35: Annual Conference on Neural Information Processing Systems 2022,...

    2022

  3. [3]

    Borgeaud, A

    S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. B. Van Den Driess- che, J.-B. Lespiau, B. Damoc, A. Clark, D. De Las Casas, A. Guy, J. Menick, R. Ring, T. Hennigan, S. Huang, L. Maggiore, C. Jones, A. Cassirer, A. Brock, M. Paganini, G. Irving, O. Vinyals, S. Osindero, K. Simonyan, J. Rae, E. Elsen, and L. Sifre. Improving lang...

    2022

  4. [4]

    N. D. Cao, G. Izacard, S. Riedel, and F. Petroni. Autoregressive entity retrieval. In 9th Interna- tional Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7,

    2021

  5. [5]

    W.-S. Chao, Z. Zheng, H. Zhu, and H. Liu. Make large language model a better ranker, 2024. URL https://arxiv.org/abs/2403.19181

    arXiv 2024

  6. [6]

    J. Chen, H. Lin, X. Han, and L. Sun. Benchmarking large language models in retrieval- augmented generation, 2023. URL https://arxiv.org/abs/2309.01431

    arXiv 2023

  7. [7]

    S. Chen, B. J. Gutiérrez, and Y . Su. Attention in large language models yields efficient zero-shot re-rankers, 2024. URL https://arxiv.org/abs/2410.02642

    arXiv 2024

  8. [8]

    Devlin, M.-W

    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, and T. Solorio, editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, V olume 1 (Long and Short Pape...

    work page 2019

Show all 83 references
  1. [9]

    Drozdov, H

    A. Drozdov, H. Zhuang, Z. Dai, Z. Qin, R. Rahimi, X. Wang, D. Alon, M. Iyyer, A. McCallum, D. Metzler, and K. Hui. Parade: Passage ranking using demonstrations with large language models, 2023. URL https://arxiv.org/abs/2310.14408

    2023 arXiv

  2. [10]

    A. Fan, Y . Jernite, E. Perez, D. Grangier, J. Weston, and M. Auli. ELI5: long form question answering. In A. Korhonen, D. R. Traum, and L. Màrquez, editors, Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28...

    2019

  3. [11]

    Gangi Reddy, J

    R. Gangi Reddy, J. Doo, Y . Xu, M. A. Sultan, D. Swain, A. Sil, and H. Ji. FIRST: Faster improved listwise reranking with single token decoding. In Y . Al-Onaizan, M. Bansal, and Y .-N. Chen, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language ...

    2024 doi

  4. [12]

    J. Gao, L. Li, W. Li, Y . Fu, and B. Dai. Smartrag: Jointly learn rag-related tasks from the environment feedback, 2024. URL https://arxiv.org/abs/2410.18141

    2024 arXiv

  5. [13]

    Grattafiori, A

    A. Grattafiori, A. Dubey, A. Jauhri, et al. The llama 3 herd of models, 2024. URL https: //arxiv.org/abs/2407.21783. 12

    2024 arXiv

  6. [14]

    K. Guu, K. Lee, Z. Tung, P. Pasupat, and M.-W. Chang. Realm: retrieval-augmented language model pre-training. In International Conference on Machine Learning , 2020

    2020

  7. [15]

    X. Ho, A. D. Nguyen, S. Sugawara, and A. Aizawa. Constructing A multi-hop QA dataset for comprehensive evaluation of reasoning steps. In D. Scott, N. Bel, and C. Zong, editors, Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelon...

    2020

  8. [16]

    Izacard, M

    G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, and E. Grave. Unsuper- vised dense information retrieval with contrastive learning. Trans. Mach. Learn. Res., 2022,

    2022

  9. [17]

    Izacard, P

    G. Izacard, P. Lewis, M. Lomeli, L. Hosseini, F. Petroni, T. Schick, J. Dwivedi-Yu, A. Joulin, S. Riedel, and E. Grave. Atlas: Few-shot learning with retrieval augmented language models. Journal of Machine Learning Research, pages 1–43, 2023

    2023

  10. [18]

    Jiang, F

    Z. Jiang, F. Xu, L. Gao, Z. Sun, Q. Liu, J. Dwivedi-Yu, Y . Yang, J. Callan, and G. Neubig. Active retrieval augmented generation. In H. Bouamor, J. Pino, and K. Bali, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , Singapore, Dec

    2023

  11. [19]

    Joshi, E

    M. Joshi, E. Choi, D. S. Weld, and L. Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In R. Barzilay and M. Kan, editors, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, V...

    2017

  12. [20]

    Karpukhin, B

    V . Karpukhin, B. Oguz, S. Min, P. S. H. Lewis, L. Wu, S. Edunov, D. Chen, and W. Yih. Dense passage retrieval for open-domain question answering. In B. Webber, T. Cohn, Y . He, and Y . Liu, editors,Proceedings of the 2020 Conference on Empirical Methods in Natural Language Pr...

    2020

  13. [21]

    Z. Ke, W. Kong, C. Li, M. Zhang, Q. Mei, and M. Bendersky. Bridging the preference gap between retrievers and llms. In L. Ku, A. Martins, and V . Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V olume 1: Long Papers)...

    2024 doi

  14. [22]

    Khandelwal, O

    U. Khandelwal, O. Levy, D. Jurafsky, L. Zettlemoyer, and M. Lewis. Generalization through memorization: Nearest neighbor language models. In International Conference on Learning Representations (ICLR), 2020

    2020

  15. [23]

    Khramtsova, S

    E. Khramtsova, S. Zhuang, M. Baktashmotlagh, and G. Zuccon. Leveraging llms for unsuper- vised dense retriever ranking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , SIGIR 2024, page 1307–1317. ACM, July 20...

    2024

  16. [24]

    Kulkarni, P

    M. Kulkarni, P. Tangarajan, K. Kim, and A. Trivedi. Reinforcement learning for optimizing rag for domain chatbots. arXiv preprint arXiv:2401.06800, 2024

    2024 arXiv

  17. [25]

    Kwiatkowski, J

    T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. P. Parikh, C. Alberti, D. Epstein, I. Polosukhin, J. Devlin, K. Lee, K. Toutanova, L. Jones, M. Kelcey, M. Chang, A. M. Dai, J. Uszkoreit, Q. Le, and S. Petrov. Natural questions: a benchmark for question answering resea...

    2019

  18. [26]

    Lewis, E

    P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela. Retrieval-augmented generation for knowledge- intensive nlp tasks. In Advances in Neural Information Processing Systems , volume 33, pa...

    2020

  19. [27]

    Liang, R

    P. Liang, R. Bommasani, T. Lee, D. Tsipras, D. Soylu, M. Yasunaga, Y . Zhang, D. Narayanan, Y . Wu, A. Kumar, B. Newman, B. Yuan, B. Yan, C. Zhang, C. Cosgrove, C. D. Manning, C. Ré, D. Acosta-Navas, D. A. Hudson, E. Zelikman, E. Durmus, F. Ladhak, F. Rong, H. Ren, H. Yao, J. ...

    2023 arXiv

  20. [28]

    C.-Y . Lin. ROUGE: A package for automatic evaluation of summaries. InText Summarization Branches Out, Barcelona, Spain, July 2004. Association for Computational Linguistics

    2004

  21. [29]

    X. V . Lin, X. Chen, M. Chen, W. Shi, M. Lomeli, R. James, P. Rodriguez, J. Kahn, G. Szilvasy, M. Lewis, L. Zettlemoyer, and W. Yih. RA-DIT: retrieval-augmented dual instruction tuning. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Aus...

    2024

  22. [30]

    W. Liu, Y . Zhu, and Z. Dou. Demorank: Selecting effective demonstrations for large language models in ranking task, 2024. URL https://arxiv.org/abs/2406.16332

    2024 arXiv

  23. [31]

    Y . Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V . Stoyanov. Roberta: A robustly optimized bert pretraining approach, 2019

    2019

  24. [32]

    Z. Liu, W. Ping, R. Roy, P. Xu, C. Lee, M. Shoeybi, and B. Catanzaro. Chatqa: Surpassing gpt-4 on conversational qa and rag, 2024. URL https://arxiv.org/abs/2401.10225

    2024 arXiv

  25. [33]

    X. Ma, X. Zhang, R. Pradeep, and J. Lin. Zero-shot listwise document reranking with a large language model, 2023. URL https://arxiv.org/abs/2305.02156

    2023 arXiv

  26. [34]

    X. Ma, L. Wang, N. Yang, F. Wei, and J. Lin. Fine-tuning llama for multi-stage text retrieval. In G. H. Yang, H. Wang, S. Han, C. Hauff, G. Zuccon, and Y . Zhang, editors,Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retr...

    2024

  27. [35]

    Mihaylov, P

    T. Mihaylov, P. Clark, T. Khot, and A. Sabharwal. Can a suit of armor conduct electricity? A new dataset for open book question answering. In E. Riloff, D. Chiang, J. Hockenmaier, and J. Tsujii, editors, Proceedings of the 2018 Conference on Empirical Methods in Natural Langua...

    2018

  28. [36]

    Nguyen, P

    T. Nguyen, P. Chin, and Y .-W. Tai. Reward-rag: Enhancing rag with reward driven supervision,

  29. [37]

    R. F. Nogueira, Z. Jiang, R. Pradeep, and J. Lin. Document ranking with a pretrained sequence- to-sequence model. In T. Cohn, Y . He, and Y . Liu, editors, Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020 , volume EMNLP 2...

    2020

  30. [38]

    Hurst, A

    OpenAI, :, A. Hurst, A. Lerer, A. P. Goucher, A. Perelman, A. Ramesh, et al. Gpt-4o system card, 2024. URL https://arxiv.org/abs/2410.21276

    2024 arXiv

  31. [39]

    Achiam, S

    OpenAI, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, et al. Gpt-4 technical report,

  32. [40]

    Ouyang, J

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe. Training language models to follow instructions ...

    2022 arXiv

  33. [41]

    Petroni, A

    F. Petroni, A. Piktus, A. Fan, P. S. H. Lewis, M. Yazdani, N. D. Cao, J. Thorne, Y . Jernite, V . Karpukhin, J. Maillard, V . Plachouras, T. Rocktäschel, and S. Riedel. KILT: a benchmark for knowledge intensive language tasks. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. ...

    2021

  34. [42]

    Pradeep, S

    R. Pradeep, S. Sharifymoghaddam, and J. Lin. Rankvicuna: Zero-shot listwise document reranking with open-source large language models, 2023. URL https://arxiv.org/abs/ 2309.15088

    2023 arXiv

  35. [43]

    Z. Qin, R. Jagerman, K. Hui, H. Zhuang, J. Wu, L. Yan, J. Shen, T. Liu, J. Liu, D. Metzler, X. Wang, and M. Bendersky. Large language models are effective text rankers with pairwise ranking prompting. In K. Duh, H. Gomez, and S. Bethard, editors, Findings of the Association fo...

    2024

  36. [44]

    Rafailov, A

    R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36, 2024

    2024

  37. [45]

    URL https://arxiv.org/abs/2303.08774

    arXiv

  38. [46]

    S. E. Robertson and H. Zaragoza. The probabilistic relevance framework: BM25 and beyond. F ound. Trends Inf. Retr ., 3(4):333–389, 2009. doi: 10.1561/1500000019. URL https://doi. org/10.1561/1500000019

    2009 doi

  39. [47]

    D. S. Sachan, M. Lewis, M. Joshi, A. Aghajanyan, W. tau Yih, J. Pineau, and L. Zettlemoyer. Improving passage retrieval with zero-shot question generation, 2023. URL https://arxiv. org/abs/2204.07496

    2023 arXiv

  40. [48]

    E. Song, S. Kim, H. Lee, J. Kim, and J. Thorne. Re3val: Reinforced and reranked generative retrieval. In Y . Graham and M. Purver, editors, Findings of the Association for Compu- tational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 , pages 393–409. Associati...

    2024

  41. [49]

    Stelmakh, Y

    I. Stelmakh, Y . Luan, B. Dhingra, and M. Chang. ASQA: factoid questions meet long-form an- swers. In Y . Goldberg, Z. Kozareva, and Y . Zhang, editors,Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Em...

    2022

  42. [50]

    doi: 10.18653/v1/2024.findings-naacl.97

    Association for Computational Linguistics. doi: 10.18653/v1/2024.findings-naacl.97. URL https://aclanthology.org/2024.findings-naacl.97/

    2024 doi

  43. [51]

    W. Sun, L. Yan, X. Ma, et al. Is ChatGPT good at search? investigating large language models as re-ranking agents. In H. Bouamor, J. Pino, and K. Bali, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages 14918–14937, Singapor...

    2023 doi

  44. [52]

    O. Ram, Y . Levine, I. Dalmedigos, D. Muhlgay, A. Shashua, K. Leyton-Brown, and Y . Shoham. In-context retrieval-augmented language models. Transactions of the Association for Computa- tional Linguistics, pages 1316–1331, 2023

    2023

  45. [53]

    Thorne, A

    J. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal. FEVER: a large-scale dataset for fact extraction and verification. In M. A. Walker, H. Ji, and A. Stent, editors, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational ...

    2018

  46. [54]

    Touvron, L

    H. Touvron, L. Martin, K. Stone, et al. Llama 2: Open foundation and fine-tuned chat models,

  47. [55]

    Trivedi, N

    H. Trivedi, N. Balasubramanian, T. Khot, and A. Sabharwal. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. In A. Rogers, J. Boyd- Graber, and N. Okazaki, editors, Proceedings of the 61st Annual Meeting of the Association for...

    2023 doi

  48. [56]

    Z. Xi, Y . Ding, W. Chen, B. Hong, H. Guo, J. Wang, D. Yang, C. Liao, X. Guo, W. He, S. Gao, L. Chen, R. Zheng, Y . Zou, T. Gui, Q. Zhang, X. Qiu, X. Huang, Z. Wu, and Y .-G. Jiang. Agentgym: Evolving large language model-based agents across diverse environments, 2024. URL htt...

    2024 arXiv

  49. [57]

    W. Su, Y . Tang, Q. Ai, Z. Wu, and Y . Liu. Dragin: Dynamic retrieval augmented generation based on the information needs of large language models, 2024. URL https://arxiv.org/ abs/2403.10081

    2024 arXiv

  50. [58]

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y . Cao. React: Synergizing rea- soning and acting in language models, 2023. URL https://arxiv.org/abs/2210.03629

    2023 arXiv

  51. [59]

    Taori, I

    R. Taori, I. Gulrajani, T. Zhang, Y . Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/ stanford_alpaca, 2023. 15

    2023

  52. [60]

    Y . Yu, W. Ping, Z. Liu, B. Wang, J. You, C. Zhang, M. Shoeybi, and B. Catanzaro. Rankrag: Unifying context ranking with retrieval-augmented generation in llms, 2024. URL https: //arxiv.org/abs/2407.02485

    2024 arXiv

  53. [61]

    A. Zeng, M. Liu, R. Lu, B. Wang, X. Liu, Y . Dong, and J. Tang. Agenttuning: Enabling generalized agent abilities for llms, 2023. URL https://arxiv.org/abs/2310.12823

    2023 arXiv

  54. [62]

    URL https://arxiv.org/abs/2307.09288

    arXiv

  55. [63]

    SUPPORTS

    T. Zhang, S. G. Patil, N. Jain, S. Shen, M. Zaharia, I. Stoica, and J. E. Gonzalez. Raft: Adapting language model to domain specific rag. arXiv preprint arXiv:2403.10131, 2024. 16 A Appendix A.1 Algorithm The algorithm of our main method is shown in Algorithm 1. Algorithm 1 Dy...

    2024 arXiv

  56. [65]

    Z. Yang, P. Qi, S. Zhang, Y . Bengio, W. W. Cohen, R. Salakhutdinov, and C. D. Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In E. Riloff, D. Chiang, J. Hockenmaier, and J. Tsujii, editors, Proceedings of the 2018 Conference on Empirical M...

    2018

  57. [67]

    T. Yu, S. Zhang, and Y . Feng. Auto-rag: Autonomous retrieval-augmented generation for large language models, 2024. URL https://arxiv.org/abs/2411.19443

    2024 arXiv

  58. [70]

    Zhang, V

    T. Zhang, V . Kishore, F. Wu, K. Q. Weinberger, and Y . Artzi. Bertscore: Evaluating text generation with BERT. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020

    2020

  59. [73]

    Title: { title } Content: { content } ... Top-N. Title: { title } Content: { content } Table 12: Prompt template for retrieval-based generator. Retrieval-based Generator Prompt You are an intelligent assistant that uses retrieved knowledge to answer user queries accurately and...

  60. [74]

    I cannot fully answer based on the available information. Please provide more details

    Task: • Use the provided [Retrieved Content] to generate responses. • If the Retrieved Content is None, you should generate an answer based on your own knowledge. • If the information is insufficient or you don’t know the answer, state, “I cannot fully answer based on the avai...

  61. [75]

    • Conciseness: Keep answers brief and relevant

    Requirements: • Accuracy: Base your answers on the retrieved content. • Conciseness: Keep answers brief and relevant. • Context Awareness: Ensure your responses align with the user’s query

  62. [76]

    Input Format: • Query: [User Query] • Retrieved: [Retrieved Content]

  63. [77]

    Always prioritize clarity and reliability

    Output Format: • A structured, clear response tailored to the query. Always prioritize clarity and reliability. Query: { Question } Retrieved Content:

  64. [78]

    Title: { title } Content: { content } ... Top-K. Title: { title } Content: { content } 23 Table 13: Prompt template for our designed reward function. Reward Function Prompt Use the following criteria to evaluate the quality of the model’s response in a knowledge- intensive tas...

  65. [79]

    Deduct points if the response lacks focus or deviates significantly from the query

    Relevance to the Prompt (20 points): Award up to 20 points if the response aligns well with the user’s query, even if minor errors are present. Deduct points if the response lacks focus or deviates significantly from the query

  66. [80]

    Penalize for inaccuracies, missing essential elements, or presenting incorrect knowledge

    Accuracy of Factual Information (20 points): Grant up to 20 points for correct factual details aligning with the ground-truth answer. Penalize for inaccuracies, missing essential elements, or presenting incorrect knowledge

  67. [81]

    Deduct points if temporal reasoning is flawed or logical consistency is missing

    Handling of Temporal and Logical Reasoning (20 points): Award up to 20 points for demonstrating correct temporal and logical reasoning. Deduct points if temporal reasoning is flawed or logical consistency is missing

  68. [82]

    Reduce points for ambiguity, confusion, or poor organization

    Clarity and Coherence of Response (20 points): Assign up to 15 points for clear, coherent, and well-structured responses. Reduce points for ambiguity, confusion, or poor organization

  69. [83]

    Score: points

    Potential Misleading Nature or Misconceptions (20 points): Award up to 10 points if the response avoids being misleading. Penalize responses that could confuse or mislead the user, even if partially relevant. After evaluating the response based on these criteria, provide a tot...

  70. [1991]

    Musical Healing

    This was followed by "Moods 2" in 1992. The series focuses on the genres of new-age, ambient, world music, and to a lesser extent, downtempo, trip-hop and smooth jazz. Doc 8: Christina Lux, Alquimia and Gleisberg; and several compilations of meditative and world-influenced New...

    1992

  71. [2020]

    URL https://doi.org/10.18653/ v1/2020.findings-emnlp.63

    doi: 10.18653/V1/2020.FINDINGS-EMNLP.63. URL https://doi.org/10.18653/ v1/2020.findings-emnlp.63

    2020 doi

  72. [2021]

    URL https://openreview.net/forum?id=5k8F6UU39V

    OpenReview.net, 2021. URL https://openreview.net/forum?id=5k8F6UU39V

    2021

  73. [2022]

    URL https://openreview.net/forum?id=jKN1pXi7b0

  74. [2023]

    Association for Computational Linguistics

  75. [2024]

    URL https://arxiv.org/abs/2410.03780

    arXiv

Pith tools

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