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REVIEW 4 major objections 7 minor 2 cited by

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that rewriting and decomposing noisy user queries with an LLM before retrieval makes live retrieval-augmented generation reliable, and reports its Omni-RAG system placed second among twelve teams in the SIGIR 2025…

desk verdict A live RAG competition system with a credible rank-2 result and a clear recipe, but the paper's causal claim about query understanding is not supported by its internal evidence. read the letter →

arxiv 2506.21384 v1 pith:QSZQYEBT submitted 2025-06-26 cs.CL cs.AIcs.IR

Guanting Dong , Xiaoxi Li , Yuyao Zhang , Mengjie Deng This is my paper
classification cs.CLcs.AIcs.IR
keywords Retrieval-AugmentedGenerationQueryUnderstandingDecompositionDenoisingLiveRAGChallengeDocumentRankingOpenSearch
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

Omni-RAG is a retrieval-augmented generation framework built for live, open-domain settings where user queries contain typos, ambiguous phrasing, and several intents at once. The paper's central claim is that cleaning and splitting such queries with an LLM before retrieval is what lets a RAG system answer them well. The full pipeline rewrites and decomposes the query, retrieves per sub-query over the FineWeb corpus with OpenSearch, reranks with BGE, and lets Falcon-10B compose the final answer. The reported evidence is a second-place overall finish among twelve teams in Session 1 of the SIGIR 2025 LiveRAG Challenge.

What carries the argument

The load-bearing component is the Deep Query Understanding and Decomposition module, in which an LLM rewrites the query $q$ into a clean form $q'$ and then splits it into $M$ sub-queries $\{q'_s\}_{s=1}^M$ using tailored prompts. This module converts one ambiguous query into several specific retrieval targets, which is what allows the subsequent OpenSearch retrieval, BGE reranking, and Falcon-10B chain-of-thought generation to cover all intents. The paper formalizes the two steps as $q' = \mathrm{Rewrite}(q, \theta_{\mathrm{rewrite}})$ and $\{q'_s\}_{s=1}^M = \mathrm{Decompose}(q', \theta_{\mathrm{decompose}})$.

What would settle it

Run the identical OpenSearch, BGE, and Falcon-10B stack on the same LiveRAG queries with the rewriting/decomposition step disabled; if correctness and faithfulness stay at the same level, the query-understanding module is not the source of the system's performance.

Watch

Extended reading notes

Core claim

The paper introduces Omni-RAG, a three-module pipeline whose distinguishing step is LLM-assisted query understanding. Instead of sending the raw user query to the retriever, an LLM rewrites it into a cleaner form and decomposes it into a set of sub-queries, each targeting one intent. Retrieval then runs separately for every sub-query, and the union of results is reranked and trimmed before generation. The paper's claim is that this preprocessing is the reason the system handles the noisy, multi-intent queries generated by the LiveRAG simulator, and that the pipeline demonstrates a practical route to live RAG. The reported outcome is a second-place score in Session 1 of the challenge, with higher correctness and faithfulness than most competing systems.

Load-bearing premise

The system's advantage is credited to LLM-based rewriting and decomposition, but the paper does not ablate that module, so nothing in the reported experiments shows it causes the rank-2 scores.

Editorial extensions

If this is right

  • If the query-understanding module is the cause of the rank-2 result, then any live RAG system that faces noisy or multi-intent queries should insert an LLM-based rewriting and decomposition step before retrieval.
  • The system shows that a fixed set of standard components, such as OpenSearch, a BGE reranker, and a sub-10B-parameter generator, can be competitive in a live RAG setting when the query is preprocessed well.
  • The dry-test analysis suggests that performance keeps improving as the number of retrieved documents per sub-query grows, supporting the union-of-sub-queries retrieval design over a single-shot search.
  • The self-consistency experiments indicate that more sampling paths does not always help: four paths improved relevance, while eight did not, so the path count needs to be tuned rather than raised blindly.

Reading between the lines

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

  • The paper never compares the full pipeline against the same pipeline with the rewriting/decomposition module removed, so the rank-2 result may be driven by other components rather than by query understanding; this is the key open test for the paper's causal claim.
  • Decomposing a query into independent sub-queries suggests a natural extension: chain the sub-queries so that evidence retrieved for one sub-query informs the reformulation of the next, turning the decomposition into an iterative search plan.
  • The pseudo-labeling evaluation with Qwen models could be validated by comparing its scores against human ratings on a small labeled sample; if it tracks human judgment, it becomes a reusable way to evaluate live RAG systems that have no ground-truth answers.
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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

4 major / 7 minor

Summary. The paper presents Omni-RAG, a retrieval-augmented generation pipeline submitted to the SIGIR 2025 LiveRAG Challenge. The pipeline consists of LLM-based query rewriting and decomposition, per-sub-query retrieval from FineWeb using OpenSearch, BGE reranking, and Falcon-10B generation with chain-of-thought prompting. The authors report that their team (RUC_DeepSearch) achieved rank 2 in Session 1 of the challenge, and they supplement this with dry-test pseudo-labeling experiments using 50 samples. The central claimed contribution is that LLM-assisted query understanding (denoising and decomposition) improves retrieval and final answer quality in live, noisy, multi-intent settings.

Significance. The externally judged rank-2 result on the LiveRAG leaderboard is a genuine and objective piece of evidence that the overall pipeline is competitive; this is the paper's main strength. The paper also addresses a practically important problem: handling noisy and multi-intent queries in live RAG. However, the manuscript does not currently establish that query understanding is the cause of the system's success, and the described architecture contains an internal tension between per-sub-query retrieval and monolithic reranking. If the authors add a proper ablation and resolve the reranking inconsistency, the rank-2 result could support a useful systems paper, but as written the evidence is insufficient to credit the claimed mechanism.

major comments (4)
  1. [§3.4, Eq. (5)] The reranker is described as computing score(q, d) against 'the original query q (or the rewritten query q')' rather than against each sub-query q'_s. Since all documents from all sub-queries are then ranked by a single monolithic query and only the top-N are kept, documents that are highly relevant to a minority sub-intent can receive low scores and be pruned, effectively undoing the decomposition step before generation. This is not merely a missing ablation; it is an internal inconsistency in the described pipeline, because the claimed benefit of intent-aware decomposition cannot be realized as specified. The authors must specify which query representation is actually used for reranking, or modify the architecture so that reranking respects sub-intents (e.g., per-sub-query reranking followed by a merge).
  2. [§3.2, §3.6, Table 1] No experiment isolates the effect of the query understanding module. Tables 1 and 2 compare different top-k settings and self-consistency path counts, but there is no condition that runs OpenSearch, BGE, and Falcon-10B with and without rewriting/decomposition (Eqs. 2-3). The rank-2 leaderboard result therefore cannot be attributed to LLM-assisted query understanding; it may be entirely due to the retrieval, reranking, and generation configuration. The authors should add an ablation that removes or bypasses the rewriting/decomposition module, and report both dry-test pseudo-label scores and any available leaderboard-based comparison.
  3. [§3.5 vs. §3.6, Table 2] The pseudo-labeling protocol is described inconsistently. Section 3.5 says Qwen2.5-7B-Instruct generates reference answers and Falcon-10B performs relevance and faithfulness evaluations, but Table 2 states that the in-house metrics are generated by Qwen2.5-72B-Instruct. It is unclear which model produced the reference answers, which model scored them, and whether the dry-test analysis in §3.6 uses a different protocol from §3.5. This discrepancy must be resolved before the reported numbers can be interpreted.
  4. [§3.5, 'Dry Test Analysis'] The dry-test evaluation is based on only 50 samples and uses a circular reference: Qwen2.5-7B generates pseudo-answers from the same top-k documents that the system retrieves, and another LLM then scores the system's answers against those pseudo-answers. This does not provide independent evidence of factual correctness, and the paper does not report confidence intervals or statistical tests for the claim that performance 'scales with document count.' The authors should either use official ground truth where available, add human spot-checking, or at minimum report bootstrap intervals and a significance test.
minor comments (7)
  1. [§1, reference [32]] The introduction cites [32] for Falcon3-10B-Instruct, but [32] is the RefinedWeb dataset paper; the model citation appears incorrect.
  2. [§3, paragraph 1] The framework is called 'Omini-RAG' in the overview paragraph, while the rest of the paper uses 'Omni-RAG'; please standardize the spelling.
  3. [§3.6, main result paragraph] The text refers to 'our RobustRAG framework' in the paragraph following Table 1; this should be 'Omni-RAG'.
  4. [Table 2] The table columns are not self-explanatory: it is unclear whether the numbers after top-k are counts of samples in each score category and whether the first column under 'Avg' is the total score or another aggregate. Please add a full caption and define every column.
  5. [Eqs. (2)-(3)] The notation θ_rewrite and θ_decompose suggests fine-tuned parameters, but the method section describes prompt-based rewriting and decomposition; if no fine-tuning is performed, the notation should be changed to avoid implying trained components.
  6. [§3.4, Eq. (5)] The introduction states that the top-10 documents are selected, while Eq. (5) uses a generic top-N; please reconcile the notation and state the actual value used in the LiveRAG submission.
  7. [Reference [42]] The self-consistency strategy is attributed to [42], but [42] is the Self-Instruct paper; the appropriate citation is Wang et al. 2022, 'Self-Consistency Improves Chain of Thought Reasoning in Language Models.'

Circularity Check

1 steps flagged · score 2.0 of 10

The Rank-2 leaderboard result is externally judged and not circular; only the dry-test pseudo-label evaluation is internally self-referential, and it is not load-bearing for the primary claim.

  1. other [Section 3.5 (Pseudo Labeling and Evaluation) and Table 2 (Performance comparison)]
    "we first adopt Qwen2.5-7B-Instruct ... as the reference model to generate pseudo answers by feeding it the input query along with its retrieved documents. ... Finally, we employ Falcon-10B to independently execute both relevance and faithfulness evaluations, generating pseudo scores for each candidate answer accordingly."

    The dry-test evaluation is a closed loop: the reference (golden) answer is generated by an LLM conditioned on the same retrieved document set that the Omni-RAG pipeline itself retrieved, and the evaluation scores are produced by comparing the system's prediction to that self-generated reference (using either Falcon-10B or, in Table 2, Qwen2.5-72B-Instruct). Thus the internal 'relevance' and 'faithfulness' numbers in Table 2 measure agreement with a model-generated answer built from the same evidence, not correspondence to external ground truth.

full rationale

The paper's primary claim — Rank-2 in Session 1 of the SIGIR LiveRAG Challenge — is judged by an external leaderboard against fixed official metrics, so it is not circular. The query-understanding, retrieval, reranking, and generation equations (Eqs. 2-6) are descriptive definitions of a pipeline, not derivations that assume their own conclusions. The authors cite several of their own prior papers in the related-work section, but none is load-bearing for the framework's design or for the competitive result; those citations are normal scholarly context. The most notable circularity-adjacent issue is the pseudo-labeling evaluation in Section 3.5 and Table 2: reference answers are generated from the same retrieved documents the system uses, and scores are assigned by LLMs comparing to those self-generated references. This is a self-referential evaluation loop, but it is explicitly confined to dry-test analysis and is not the basis of the Rank-2 claim. The absence of an ablation isolating query rewriting/decomposition is a weakness in attribution, not circularity: the leaderboard rank is an external outcome, and the paper does not claim to have derived that rank from the query-understanding module by construction. Overall, the central claim has independent external support, so the circularity score is low.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The framework contributes no new scientific object; its stated novelty is the combination of known components (LLM rewriting/decomposition, OpenSearch retrieval, BGE reranking, CoT generation). The load-bearing assumptions are the domain assumptions that rewriting and decomposition help retrieval, and the ad-hoc assumption that LLM pseudo-labels are trustworthy proxies for the official metrics. Free parameters are the retrieval and generation cutoffs, tuned on the dry-test pseudo-labels.

free parameters (3)
  • top-K retrieved per sub-query = 1-5
    Table 2 sweeps top-K from 1 to 5 and shows scores rising with K; the final setting for the challenge is chosen from this dry-test sweep.
  • top-N documents passed to generator = 10 (challenge), 5 (dry test)
    Section 1 states the generator receives the top-10 reranked documents, while Section 3.6 uses top-5 in the dry test; the cutoff is hand-set and affects quality.
  • self-consistency path count = 4 or 8
    Table 2 tests 5(sc4) and 5(sc8); sc4 raises relevance but lowers faithfulness, so the choice is tuned on pseudo-labels rather than justified by a principle.
assumptions (5)
  • domain assumption LLM rewriting improves retrieval quality for noisy queries.
    Invoked in Eq. (2) and Module 1; the paper does not ablate rewriting, so the assumption is untested.
  • domain assumption Decomposing a multi-intent query into sub-queries and retrieving each improves document recall.
    Invoked in Eq. (3) and Section 3.3; the number of sub-queries and their marginal value are not evaluated.
  • domain assumption Reranking with BGE improves the final document set relative to raw retrieval order.
    Section 3.4 applies BGE-reranker-large without comparing to no reranking.
  • ad hoc to paper Pseudo-labels produced by LLMs approximate the official LiveRAG evaluation metrics.
    Section 3.5 introduces pseudo-label scoring to evaluate the dry test; its agreement with official metrics is not validated.
  • domain assumption Falcon-10B with a chain-of-thought prompt produces better-scoring answers than other prompt choices.
    Section 3.4 fixes the generator and prompt style; no prompt ablations are reported.

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Cite this review

Pith. "Pith review of Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation." pith.science (2026). https://pith.science/paper/QSZQYEBT

@misc{pith2026250621384,
  author       = {Pith},
  title        = {Pith review of: Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QSZQYEBT}},
  note         = {Machine review of arXiv:2506.21384}
}
read the original abstract

Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language models (LLMs) with external knowledge, current systems typically struggle with such complex inputs, as they are often trained or evaluated on cleaner data. This paper introduces Omni-RAG, a novel framework designed to improve the robustness and effectiveness of RAG systems in live, open-domain settings. Omni-RAG employs LLM-assisted query understanding to preprocess user inputs through three key modules: (1) Deep Query Understanding and Decomposition, which utilizes LLMs with tailored prompts to denoise queries (e.g., correcting spelling errors) and decompose multi-intent queries into structured sub-queries; (2) Intent-Aware Knowledge Retrieval, which performs retrieval for each sub-query from a corpus (i.e., FineWeb using OpenSearch) and aggregates the results; and (3) Reranking and Generation, where a reranker (i.e., BGE) refines document selection before a final response is generated by an LLM (i.e., Falcon-10B) using a chain-of-thought prompt. Omni-RAG aims to bridge the gap between current RAG capabilities and the demands of real-world applications, such as those highlighted by the SIGIR 2025 LiveRAG Challenge, by robustly handling complex and noisy queries.

Figures

Figures reproduced from arXiv: 2506.21384 by the authors.

Figure 1
Figure 1. Given a query, an LLM first performs deep understanding, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 1
Figure 1. The overall pipeline of our Omni-RAG. structured format, such as JSON, for easy extraction and process￾ing. This lays a solid foundation for retrieving broader and more accurate knowledge during the search process. 3.3 Intent-Aware Knowledge Retrieval To obtain more comprehensive and extensive information, an intu￾itive approach is to retrieve information for each sub-intent derived from the decomposition of a compl… view at source ↗

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Forward citations

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Reference graph

Works this paper leans on

53 extracted references · 16 canonical work pages · cited by 2 Pith papers

  1. [1]

    Abhijit Anand, Venktesh V, Vinay Setty, and Avishek Anand. 2023. Context Aware Query Rewriting for Text Rankers using LLM.CoRR abs/2308.16753 (2023). https://doi.org/10.48550/ARXIV.2308.16753 arXiv:2308.16753

    arXiv doi:10.48550/arxiv.2308.16753 2023

  2. [2]

    Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi

  3. [3]

    Hiteshwar Kumar Azad and Akshay Deepak. 2019. Query expansion techniques for information retrieval: A survey. Inf. Process. Manag. 56, 5 (2019), 1698–1735. https://doi.org/10.1016/J.IPM.2019.05.009

    doi:10.1016/j.ipm.2019.05.009 2019

  4. [4]

    Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng X...

    arXiv doi:10.48550/arxiv.2309.16609 2023

  5. [5]

    Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Ruther- ford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bog- dan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Ro- man Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Ir...

    work page 2022

  6. [6]

    Chi-Min Chan, Chunpu Xu, Ruibin Yuan, Hongyin Luo, Wei Xue, Yike Guo, and Jie Fu. 2024. RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation. CoRR abs/2404.00610 (2024). https://doi.org/10.48550/ARXIV.2404. 00610 arXiv:2404.00610

    arXiv doi:10.48550/arxiv.2404 2024

  7. [7]

    Guanting Dong, Yifei Chen, Xiaoxi Li, Jiajie Jin, Hongjin Qian, Yutao Zhu, Hangyu Mao, Guorui Zhou, Zhicheng Dou, and Ji-Rong Wen. 2025. Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning. arXiv:2505.16410 [cs.CL] https://arxiv.org/abs/2505.16410

    arXiv 2025

  8. [8]

    Guanting Dong, Keming Lu, Chengpeng Li, Tingyu Xia, Bowen Yu, Chang Zhou, and Jingren Zhou. 2025. Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net. https://openreview.net/f...

    work page 2025

Show all 53 references
  1. [9]

    Guanting Dong, Yutao Zhu, Chenghao Zhang, Zechen Wang, Zhicheng Dou, and Ji-Rong Wen. 2024. Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation. CoRR abs/2406.18676 (2024). https://doi. org/10.48550/ARXIV.2406.18676 arXiv:2406.18676

    2024 doi arXiv

  2. [10]

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ah- mad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sra- vankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston...

    2024 doi arXiv

  3. [11]

    Simone Filice, Guy Horowitz, David Carmel, Zohar Karnin, Liane Lewin-Eytan, and Yoelle Maarek. 2025. Generating Diverse Q&A Benchmarks for RAG Evalua- tion with DataMorgana. arXiv preprint arXiv:2501.12789 (2025)

    2025 arXiv

  4. [12]

    Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2023. Precise Zero-Shot Dense Retrieval without Relevance Labels. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 ...

    2023 doi

  5. [13]

    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang. 2023. Retrieval-Augmented Generation for Large Language Models: A Survey. CoRR abs/2312.10997 (2023). https://doi.org/10.48550/ARXIV.2312.10997 arX...

    2023 doi arXiv

  6. [14]

    Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. 2023. Fid- light: Efficient and effective retrieval-augmented text generation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1437–1447

    2023

  7. [15]

    Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2023. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. CoRR abs/2311.052...

    2023 doi arXiv

  8. [16]

    Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig

    Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. Active Retrieval Aug- mented Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Si...

    2023

  9. [17]

    Bowen Jin, Jinsung Yoon, Jiawei Han, and Sercan Ö. Arik. 2025. Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG. In The Thir- teenth International Conference on Learning Representations, ICLR 2025, Singa- pore, April 24-28, 2025 . OpenReview.net. https:...

    2025

  10. [18]

    Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Qi Ye, and Zhicheng Dou. 2025. Hierarchical Document Refinement for Long-context Retrieval-augmented Generation. arXiv:2505.10413 [cs.CL] https://arxiv.org/abs/2505.10413

    2025 arXiv

  11. [19]

    Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024. Bridging the Preference Gap between Retrievers and LLMs. arXiv:2401.06954 [cs.CL]

    2024 arXiv

  12. [20]

    Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, Joonsuk Park, and Jaewoo Kang

  13. [21]

    Myeonghwa Lee, Seonho An, and Min-Soo Kim. 2024. PlanRAG: A Plan-then- Retrieval Augmented Generation for Generative Large Language Models as Deci- sion Makers. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics:...

    2024

  14. [23]

    Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. 2025. Search-o1: Agentic Search-Enhanced Large Reasoning Models. CoRR abs/2501.05366 (2025). https://doi.org/10.48550/ARXIV. 2501.05366 arXiv:2501.05366

    2025 doi arXiv

  15. [24]

    Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advanc...

    2020

  16. [25]

    Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yuyao Zhang, Peitian Zhang, Yutao Zhu, and Zhicheng Dou. 2025. From Matching to Generation: A Survey on Generative Information Retrieval. ACM Trans. Inf. Syst. 43, 3, Article 83 (May 2025), 62 pages. https://doi.org/10.1145/3722552

    2025 doi

  17. [26]

    Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yutao Zhu, Yongkang Wu, Ji- Rong Wen, and Zhicheng Dou. 2025. WebThinker: Empowering Large Reasoning Models with Deep Research Capability. CoRR abs/2504.21776 (2025). https: //doi.org/10.48550/ARXIV.2504.21776 arXiv:2504.21776

    2025 doi arXiv

  18. [27]

    Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. 2024. RaFe: Ranking Feedback Improves Query Rewriting for RAG. CoRR abs/2405.14431 (2024). https://doi. org/10.48550/ARXIV.2405.14431 arXiv:2405.14431

    2024 doi arXiv

  19. [28]

    Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023. Query Rewriting for Retrieval-Augmented Large Language Models.CoRR abs/2305.14283 (2023). https://doi.org/10.48550/ARXIV.2305.14283 arXiv:2305.14283

    2023 doi arXiv

  20. [29]

    Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019. Multi-hop Reading Comprehension through Question Decomposition and Rescor- ing. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28-...

    2019 doi

  21. [30]

    Raja Sekhar Reddy Mekala, Yasaman Razeghi, and Sameer Singh. 2024. EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Conference’17, July 2017, Washington, DC, USA Guanting Dong, Xiaoxi Li, Yuyao Zhang, and Mengjie Deng Learning. In Proceedings of th...

    2024

  22. [31]

    Guilherme Penedo, Hynek Kydlíček, Anton Lozhkov, Margaret Mitchell, Colin A Raffel, Leandro Von Werra, Thomas Wolf, et al . 2024. The fineweb datasets: Decanting the web for the finest text data at scale.Advances in Neural Information Processing Systems 37 (2024), 30811–30849

    2024

  23. [32]

    Jeonghyun Park and Hwanhee Lee. 2024. Conversational Query Reformulation with the Guidance of Retrieved Documents. CoRR abs/2407.12363 (2024). https: //doi.org/10.48550/ARXIV.2407.12363 arXiv:2407.12363

    2024 doi arXiv

  24. [33]

    Wenjun Peng, Guiyang Li, Yue Jiang, Zilong Wang, Dan Ou, Xiaoyi Zeng, Derong Xu, Tong Xu, and Enhong Chen. 2024. Large Language Model based Long-tail Query Rewriting in Taobao Search. InCompanion Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, Singapore, Ma...

    2024

  25. [34]

    Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Hamza Alobeidli, Alessandro Cappelli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023. The RefinedWeb Dataset for Falcon LLM: Out- performing Curated Corpora with Web Data Only. In Advances in...

    2023

  26. [35]

    Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, and Daxin Jiang. 2024. Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever. In Findings of the As- sociation for Computational Linguistics, ACL 2024,...

    2024 doi

  27. [36]

    Smith, and Mike Lewis

    Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. 2023. Measuring and Narrowing the Compositionality Gap in Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023 , Houda Bouamor...

    2023 doi

  28. [37]

    Mingyang Song and Mao Zheng. 2024. A Survey of Query Optimization in Large Language Models. CoRR abs/2412.17558 (2024). https://doi.org/10.48550/ARXIV. 2412.17558 arXiv:2412.17558

    2024 doi

  29. [38]

    Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023. REPLUG: Retrieval-Augmented Black-Box Language Models. CoRR abs/2301.12652 (2023). https://doi.org/10. 48550/ARXIV.2301.12652 arXiv:2301.12652

    2023 doi arXiv

  30. [39]

    Venktesh V, Sourangshu Bhattacharya, and Avishek Anand. 2023. In-Context Abil- ity Transfer for Question Decomposition in Complex QA. CoRR abs/2310.18371 (2023). https://doi.org/10.48550/ARXIV.2310.18371 arXiv:2310.18371

    2023 doi arXiv

  31. [40]

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yas- mine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhos- ale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucu- rull, David Esiobu, Jude Fernandes, Jeremy...

    2023 doi arXiv

  32. [41]

    Liang Wang, Nan Yang, and Furu Wei. 2023. Query2doc: Query Expansion with Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6- 10, 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (...

    2023 doi

  33. [42]

    Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, and Amit Sharma. 2024. Plan×RAG: Planning-guided Retrieval Aug- mented Generation. arXiv:2410.20753 [cs.CL] https://arxiv.org/abs/2410.20753

    2024 arXiv

  34. [43]

    Yile Wang, Peng Li, Maosong Sun, and Yang Liu. 2023. Self-Knowledge Guided Retrieval Augmentation for Large Language Models. InFindings of the Association for Computational Linguistics: EMNLP 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computationa...

    2023 doi

  35. [44]

    Smith, Daniel Khashabi, and Hannaneh Hajishirzi

    Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-Instruct: Aligning Language Models with Self-Generated Instructions. InProceedings of the 61st Annual Meeting of the Association for Computational Lingui...

    2023 doi

  36. [45]

    Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2024. RECOMP: Improving Retrieval- Augmented LMs with Context Compression and Selective Augmentation. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net. htt...

    2024

  37. [46]

    Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig. 2023. Learning to Filter Context for Retrieval-Augmented Generation. arXiv:2311.08377 [cs.CL]

    2023 arXiv

  38. [47]

    Narasimhan, and Yuan Cao

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . Ope...

    2023

  39. [48]

    Haoyan Yang, Zhitao Li, Yong Zhang, Jianzong Wang, Ning Cheng, Ming Li, and Jing Xiao. 2023. PRCA: Fitting Black-Box Large Language Models for Re- trieval Question Answering via Pluggable Reward-Driven Contextual Adapter. In Proceedings of the 2023 Conference on Empirical Meth...

    2023

  40. [49]

    Yuyao Zhang, Zhicheng Dou, Xiaoxi Li, Jiajie Jin, Yongkang Wu, Zhonghua Li, Qi Ye, and Ji-Rong Wen. 2025. Neuro-Symbolic Query Compiler. arXiv:2505.11932 [cs.CL] https://arxiv.org/abs/2505.11932

    2025 arXiv

  41. [50]

    Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2023. Generate rather than Retrieve: Large Language Models are Strong Context Generators. In The Eleventh International Conference on Learning Representatio...

    2023

  42. [51]

    Le, and Ed H

    Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. 2023. Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. In The Eleventh International Conference on...

    2023

  43. [52]

    Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong W...

    2023 arXiv

  44. [54]

    Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chen- long Deng, Zhicheng Dou, and Ji-Rong Wen. 2023. Large Language Mod- els for Information Retrieval: A Survey. CoRR abs/2308.07107 (2023). https: //doi.org/10.48550/ARXIV.2308.07107 arXiv:2308.07107

    2023 doi

  45. [2023]

    CoRR abs/2310.11511 (2023)

    Self-RAG: Learning to Retrieve, Generate, and Critique through Self- Reflection. CoRR abs/2310.11511 (2023). https://doi.org/10.48550/ARXIV.2310. 11511 arXiv:2310.11511

    2023 doi arXiv

Pith tools

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