Contrastive Instruction Tuning Article Swipe
Tianyi Yan
,
Wang Fei
,
James Y. Huang
,
Wenxuan Zhou
,
Fan Yin
,
Aram Galstyan
,
Wenpeng Yin
,
Muhao Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.11138
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.11138
Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks. However, current LLMs exhibit limited robustness to unseen instructions, generating inconsistent outputs when the same instruction is phrased with slightly varied forms or language styles. This behavior indicates LLMs' lack of robustness to textual variations and generalizability to unseen instructions, potentially leading to trustworthiness issues. Accordingly, we propose Contrastive Instruction Tuning, which maximizes the similarity between the hidden representations of semantically equivalent instruction-instance pairs while minimizing the similarity between semantically different ones. To facilitate this approach, we augment the existing FLAN collection by paraphrasing task instructions. Experiments on the PromptBench benchmark show that CoIN consistently improves LLMs' robustness to unseen instructions with variations across character, word, sentence, and semantic levels by an average of +2.5% in accuracy. Code is available at https://github.com/luka-group/CoIN.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.11138
- https://arxiv.org/pdf/2402.11138
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392011849
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4392011849Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.11138Digital Object Identifier
- Title
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Contrastive Instruction TuningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-02-17Full publication date if available
- Authors
-
Tianyi Yan, Wang Fei, James Y. Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, Muhao ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.11138Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2402.11138Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2402.11138Direct OA link when available
- Concepts
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Contrastive analysis, Linguistics, Computer science, Natural language processing, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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