The DOI matches its Crossref record; 90 of the 93 references it cites were found in a registry or an index.
Measuring and Improving Consistency in Pretrained Language Models
Open accessAbstract
Consistency of a model—that is, the invariance of its behavior under meaning-preserving alternations in its input—is a highly desirable property in natural language processing. In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge? To this end, we create ParaRel🤘, a high-quality resource of cloze-style query English paraphrases. It contains a total of 328 paraphrases for 38 relations. Using ParaRel🤘, we show that the consistency of all PLMs we experiment with is poor— though with high variance between relations. Our analysis of the representational spaces of PLMs suggests that they have a poor structure and are currently not suitable for representing knowledge robustly. Finally, we propose a method for improving model consistency and experimentally demonstrate its effectiveness.1
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“As a result, LLMs can struggle on commonsense knowledge benchmarks [158], especially once low-level statistical cues are controlled for [9].”
From Dissociating language and thought in large language models (2024) · cites this paper as [9]
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Transformers: State-of-the-Art Natural Language Processing
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3 of the 93 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Elsahar, 2018, T-rex: A large scale alignment of natural language with knowledge base triples
- Hewitt, 2019, A structural probe for finding syntax in word representations
- Radford, 2019, Language models are unsupervised multitask learners, OpenAI blog, 1, 9
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Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2203.11171 · Cited by 829
“, 2020), and factual knowledge extraction (Elazar et al., 2021).”
BackgroundDissociating language and thought in large language models
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.00352 · Cited by 214
Knowledge Neurons in Pretrained Transformers
Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., & Wei, F. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.581 · Cited by 187
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
Cao, Y., Zhou, L., Lee, S., Cabello, L., Chen, M., & Hershcovich, D. (2023) · Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP) · doi:10.18653/v1/2023.c3nlp-1.7 · Cited by 163
Metadata
- Authors
- Elazar, Y., Kassner, N., Ravfogel, S., Ravichander, A., Hovy, E., Schütze, H., & Goldberg, Y.
- Affiliations
- Bar-Ilan University (IL)
- Ludwig-Maximilians-Universität München (DE)
- Carnegie Mellon University (US)
- Corresponding author
- Elazar, Yanai
- Kassner, Nora
- Ravfogel, Shauli
- Ravichander, Abhilasha
- Hovy, Eduard
- Goldberg, Yoav
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2021
- Volume
- 9
- Pages
- 1012-1031
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W3202712981
- Cited by
- 220
- References
- 93
- Citations by year
- 2026: 37 · 2025: 40 · 2024: 30 · 2023: 74 · 2022: 20 · 2021: 17
- FWCI
- 12.35
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-12-06)
- Open access
- diamond
- doi.org
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1801316, 1914486, 1330596)
- Brandeis University
- European Commission (HORIZON2020)
- Bundesministerium für Bildung und Forschung (01IS18036A)
- Defense Advanced Research Projects Agency (FA8750, FA8750-15-2-0277)
- H2020 European Research Council (802774, 740516)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- pre-trained language models, consistency, factual knowledge, paraphrase, knowledge representation, representational space
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