TRUE: Re-evaluating Factual Consistency Evaluation Article Swipe
YOU?
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· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.04991
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data. While attracting increasing attention, such evaluation metrics are usually developed and evaluated in silo for a single task or dataset, slowing their adoption. Moreover, previous meta-evaluation protocols focused on system-level correlations with human annotations, which leave the example-level accuracy of such metrics unclear. In this work, we introduce TRUE: a comprehensive survey and assessment of factual consistency metrics on a standardized collection of existing texts from diverse tasks, manually annotated for factual consistency. Our standardization enables an example-level meta-evaluation protocol that is more actionable and interpretable than previously reported correlations, yielding clearer quality measures. Across diverse state-of-the-art metrics and 11 datasets we find that large-scale NLI and question generation-and-answering-based approaches achieve strong and complementary results. We recommend those methods as a starting point for model and metric developers, and hope TRUE will foster progress towards even better evaluation methods.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.04991
- https://arxiv.org/pdf/2204.04991
- OA Status
- green
- Cited By
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4229019162
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4229019162Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2204.04991Digital Object Identifier
- Title
-
TRUE: Re-evaluating Factual Consistency EvaluationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-11Full publication date if available
- Authors
-
Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, Yossi MatiasList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.04991Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2204.04991Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2204.04991Direct OA link when available
- Concepts
-
Consistency (knowledge bases), Computer science, Metric (unit), Standardization, Protocol (science), Task (project management), Information retrieval, Quality (philosophy), Point (geometry), Data science, Scale (ratio), Data mining, Machine learning, Artificial intelligence, Epistemology, Operating system, Quantum mechanics, Operations management, Pathology, Management, Medicine, Physics, Alternative medicine, Mathematics, Economics, Philosophy, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 5, 2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2204.04991 |
| publication_date | 2022-04-11 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 50, 84, 94, 155 |
| abstract_inverted_index.11 | 134 |
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| abstract_inverted_index.an | 111 |
| abstract_inverted_index.as | 154 |
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| abstract_inverted_index.on | 63, 93 |
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| abstract_inverted_index.NLI | 140 |
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| abstract_inverted_index.this | 22, 79 |
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| abstract_inverted_index.generation-and-answering-based | 143 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |