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Citation: A Key to Building Responsible and Accountable Large Language Models

Huang, Jie, Chang, Kevin·2024·Findings of the Association for Computational Linguistics: NAACL 2024
DOI 10.18653/v1/2024.findings-naacl.31
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  • The overall evaluation pipeline improves models' trustworthiness.

    “The overall evaluation pipeline helps generate post hoc citations and iteratively improve model generations' factuality, which improves models' trustworthiness model (Huang and Chang, 2024; Ye et al., 2024).”

    From VeriScore: Evaluating the factuality of verifiable claims in long-form text generation (2024) · cites this paper as Huang and Chang, 2024

    Section 4.2.1 describes post-hoc evaluation and says combining it with pre-hoc retrieval could produce more accurate, well-supported content; the Introduction says citations could bolster credibility. The paper presents these as potential benefits, not as evidence that an overall evaluation pipeline improves model trustworthiness, and it does not describe iterative improvement of generations’ factuality. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The overall evaluation pipeline helps generate post hoc citations.

    “The overall evaluation pipeline helps generate post hoc citations and iteratively improve model generations' factuality, which improves models' trustworthiness model (Huang and Chang, 2024; Ye et al., 2024).”

    From VeriScore: Evaluating the factuality of verifiable claims in long-form text generation (2024) · cites this paper as Huang and Chang, 2024

  • The overall evaluation pipeline iteratively improves model generations' factuality.

    “The overall evaluation pipeline helps generate post hoc citations and iteratively improve model generations' factuality, which improves models' trustworthiness model (Huang and Chang, 2024; Ye et al., 2024).”

    From VeriScore: Evaluating the factuality of verifiable claims in long-form text generation (2024) · cites this paper as Huang and Chang, 2024

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Research works printed but not linked: 43

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Cited by

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Authors
Huang, J., & Chang, K.
Venue
Findings of the Association for Computational Linguistics: NAACL 2024
Publisher
Association for Computational Linguistics
Published
2024
Pages
464-473
DOI
10.18653/v1/2024.findings-naacl.31
Cited by
21
References
0

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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