Unified Pragmatic Models for Generating and Following Instructions
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Daniel Fried, Jacob Andreas, Dan Klein. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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SFT can lead to harmful and unintended behavior, making it easier to prompt such models to elicit them.
“This can lead to harmful and unintended behavior, making it easier to prompt such models to elicit them [50,142].”
From RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs (2026) · cites this paper as [50]
The Introduction describes an instruction-following model reaching an unintended position because it misinterprets an ambiguous command. The paper does not connect supervised fine-tuning to harmful behavior or to prompts that make such behavior easier to elicit; searches for those terms found no passage addressing the claim. (LLM, conf=0.76)
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- Authors
- Fried, D., Andreas, J., & Klein, D.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 1951-1963
- OpenAlex
- W2962846267
- Cited by
- 64
- References
- 5
- Citations by year
- 2024: 5 · 2023: 12 · 2022: 14 · 2021: 11 · 2020: 4 · 2019: 15 · 2018: 2 · 2017: 1
- FWCI
- 3.27
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- instruction following, instruction generation, natural language understanding, natural language generation
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