Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913407031050240 |
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| author | Dutt, Ritam Wu, Zhen Shi, Kelly Sheth, Divyanshu Gupta, Prakhar Rose, Carolyn Penstein |
| author_facet | Dutt, Ritam Wu, Zhen Shi, Kelly Sheth, Divyanshu Gupta, Prakhar Rose, Carolyn Penstein |
| contents | We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_19545 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations Dutt, Ritam Wu, Zhen Shi, Kelly Sheth, Divyanshu Gupta, Prakhar Rose, Carolyn Penstein Computation and Language Artificial Intelligence We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora. |
| title | Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.19545 |