Evaluating Factuality in Generation with Dependency-level Entailment
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Despite significant progress in text generation models, a serious limitation is their tendency to produce text that is factually inconsistent with information in the input.Recent work has studied whether textual entailment systems can be used to identify factual errors; however, these sentence-level entailment models are trained to solve a different problem than generation filtering and they do not localize which part of a generation is non-factual.In this paper, we propose a new formulation of entailment that decomposes it at the level of dependency arcs.Rather than focusing on aggregate decisions, we instead ask whether the semantic relationship manifested by individual dependency arcs in the generated output is supported by the input.Human judgments on this task are difficult to obtain; we therefore propose a method to automatically create data based on existing entailment or paraphrase corpora.Experiments show that our dependency arc entailment model trained on this data can identify factual inconsistencies in paraphrasing and summarization better than sentencelevel methods or those based on question generation, while additionally localizing the erroneous parts of the generation. 1
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Sentence-level entailment models have the potential limitation of being incapable of pinpointing and locating which parts of the generation are erroneous.
“Apart from generalizability, Goyal and Durrett [90] point out the potential limitation of using sentence-level entailment models, namely their incapability to pinpoint and locate which parts of the generation are erroneous.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as Goyal and Durrett [90]
More advanced studies have proposed methods such as fine-tuning on adversarial datasets, decomposing the entailment decisions at the dependency arc level, and segmenting documents into sentence units then aggregating scores between sentence pairs.
“Building on this, more advanced studies have proposed methods such as fine-tuning on adversarial datasets [17], decomposing the entailment decisions at the dependency arc level [101], and segmenting documents into sentence units then aggregating scores between sentence pairs [154].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [101]
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- Authors
- Goyal, T., & Durrett, G.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Published
- 2020
- Pages
- 3592-3603
- OpenAlex
- W3092958231
- Cited by
- 93
- References
- 6
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 8 · 2023: 34 · 2022: 23 · 2021: 12 · 2020: 3
- FWCI
- 6.87
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1814522)
- Nvidia
- University of Texas at Austin
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- factual consistency, text generation, natural language inference, paraphrase, summarization, error localization, paraphrase corpus
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