Summary of Large Language Models Produce Responses Perceived to Be Empathic, by Yoon Kyung Lee et al.
Large Language Models Produce Responses Perceived to be Empathic
by Yoon Kyung Lee, Jina Suh, Hongli Zhan, Junyi Jessy Li, Desmond C. Ong
First submitted to arxiv on: 26 Mar 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 explores the ability of Large Language Models (LLMs) to generate empathic messages in response to posts describing common life experiences. The authors trained several models, including GPT4 Turbo, Llama2, and Mistral, to write responses that display empathy. In two studies, human raters evaluated the generated responses alongside human-written ones, rating their empathic quality. Results show that LLM-generated responses were consistently rated as more empathic than human-written ones. Linguistic analyses reveal distinct “styles” in the models’ writing, characterized by punctuation, emojis, and specific words. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how computers can write messages that are very understanding and caring. The authors had computer models generate answers to common problems people face, like work stress or relationship issues. They then asked humans to rate these responses for how empathetic they seemed. Surprisingly, the computer-generated responses were rated as more caring than ones written by humans! The study also found that these computer models have unique ways of writing, using certain words and symbols. |
