Summary of Leveraging Machine-generated Rationales to Facilitate Social Meaning Detection in Conversations, by Ritam Dutt et al.
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
by Ritam Dutt, Zhen Wu, Kelly Shi, Divyanshu Sheth, Prakhar Gupta, Carolyn Penstein Rose
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to detecting socially encoded meanings in conversations using Large Language Models (LLMs). The method involves designing multi-faceted prompts to extract textual explanations of reasoning that connects visible cues to underlying social meanings. These extracted rationales serve as augmentations to conversational text, facilitating dialogue understanding and transfer. Empirical results across 2,340 experimental settings demonstrate the positive impact of adding these rationales, showing improvements in both in-domain classification and domain transfer for two different social meaning detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big language models to understand conversations better. It’s like being able to read between the lines to figure out what people really mean when they’re talking. The researchers created a special way to ask these models questions that helps them find the hidden meanings in conversations. They tested this approach and found it worked well, even when trying to apply it to new situations or groups of people. This is important because it can help us understand each other better and communicate more effectively. |
Keywords
» Artificial intelligence » Classification
