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A Survey of Large Language Models Attribution

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arxiv 2311.03731 v2 pith:RDOPAFFZ submitted 2023-11-07 cs.CL

Dongfang Li , Zetian Sun , Xinshuo Hu , Zhenyu Liu , Ziyang Chen , Baotian Hu , Aiguo Wu , Min Zhang This is my paper · ORCID
classification cs.CL
keywords attributionsystemsgenerativefieldlanguagelargemodelsopen-domain
verification ladder T0 review T1 audit T2 compute T3 formal
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Open-domain generative systems have gained significant attention in the field of conversational AI (e.g., generative search engines). This paper presents a comprehensive review of the attribution mechanisms employed by these systems, particularly large language models. Though attribution or citation improve the factuality and verifiability, issues like ambiguous knowledge reservoirs, inherent biases, and the drawbacks of excessive attribution can hinder the effectiveness of these systems. The aim of this survey is to provide valuable insights for researchers, aiding in the refinement of attribution methodologies to enhance the reliability and veracity of responses generated by open-domain generative systems. We believe that this field is still in its early stages; hence, we maintain a repository to keep track of ongoing studies at https://github.com/HITsz-TMG/awesome-llm-attributions.

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Cited by 6 Pith papers

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

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    cs.CL 2025-05 conditional novelty 6.0 of 10

    ChartLens uses segmentation and set-of-marks prompting to attribute chart-based answers to specific visual elements, and the authors release a new benchmark for evaluating such attribution.

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    cs.CL 2025-05 conditional novelty 6.0 of 10

    A new dataset of claims with conflicting web evidence shows retrieval-augmented LLMs are fragile, and source-credibility cues help only modestly.

  3. Agentic Verification for Ambiguous Query Disambiguation

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    VERDICT disambiguates ambiguous queries for retrieval-augmented generation by verifying candidate interpretations against retrieved passages before answering, and reports big G-F1 gains on ASQA, though its evaluation ...

  4. On Mechanistic Circuits for Extractive Question-Answering

    cs.CL 2025-02 conditional novelty 6.0 of 10

    One attention head from the extracted context-faithfulness circuit provides reliable extractive QA attribution and improves context faithfulness when its attributions are added to the prompt.

  5. TokenShapley: Token Level Context Attribution with Shapley Value

    cs.CL 2025-06 conditional novelty 5.0 of 10

    TokenShapley computes token-level Shapley attributions from context to response by treating context tokens as (prefix, token) data points in a KNN datastore.

  6. heiDS at ArchEHR-QA 2025: From Fixed-k to Query-dependent-k for Retrieval Augmented Generation

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Query-dependent-k ranked list truncation performs comparably to, but not consistently better than, fixed-k retrieval in an attributed clinical RAG pipeline, with the best system still below the organizer baseline.

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