Improving Abstractive Summarization of Legal Rulings through Textual Entailment
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The problem of legal hallucination has so far only been studied in closed-domain applications.
“the problem of legal hallucination has so far only been studied in closed-domain applications, such as when a model is used to summarize the content of a given judicial opinion (Deroy, Ghosh, and Ghosh 2023; Feijo and Moreira 2023) or to synthesize provided legal text (Savelka et al. 2023)”
From Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models (2024) · cites this paper as Feijo and Moreira 2023
Cites
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741 · see citing works
On Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.173 · Cited by 1,185 · see citing works
Evaluating the Factual Consistency of Abstractive Text Summarization
Kryscinski, W., McCann, B., Xiong, C., & Socher, R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.750 · Cited by 669 · see citing works
Controllable Abstractive Summarization
Fan, A., Grangier, D., & Auli, M. (2018) · Proceedings of the 2nd Workshop on Neural Machine Translation and Generation · doi:10.18653/v1/w18-2706 · Cited by 281 · cited 1× · see citing works
“Dozens of methods have been proposed and tested on this type of document (See et al. 2017; Fan et al. 2018; Liu 2019; Zhang et al. 2020, 2019; Liu and Lapata 2019).”
MethodDid the Model Understand the Question?
Mudrakarta, P. K., Taly, A., Sundararajan, M., & Dhamdhere, K. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1176 · Cited by 166 · see citing works
Reducing Quantity Hallucinations in Abstractive Summarization
Zhao, Z., Cohen, S. B., & Webber, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.203 · Cited by 99 · see citing works
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Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models
Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024) · Journal of Legal Analysis · doi:10.1093/jla/laae003 · Cited by 288 · see citing works
- Authors
- Feijo, D. D. V., & Moreira, V. P.
- Affiliations
- Universidade Federal do Rio Grande do Sul (BR)
- Venue
- Artificial Intelligence and Law
- Published
- 2021-11-27
- Volume
- 31
- Issue
- 1
- Pages
- 91-113
- ISSN
- 0924-8463 (ISSN-L)
- 1572-8382
- OpenAlex
- W3215701899
- Cited by
- 59
- References
- 7
- Citations by year
- 2026: 7 · 2025: 25 · 2024: 14 · 2023: 10 · 2022: 3
- FWCI
- 22.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Artificial Intelligence and Law (publishedVersion)
- Topics
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- abstractive summarization, legal rulings, natural language inference, hallucinations, encoder-decoder architecture, ROUGE evaluation, judicial precedent
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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