新闻
SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue
arXiv cs.AI · 发布于 · 阅读约3分钟
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- 发生了什么
- SDARE-Bench, a new benchmark, evaluates how well large language models detect stigma and generate responses in conversations, testing 8 LLMs on 2,526 dialogue scenarios.
- 为何重要
- Matters for engineers building conversational AI systems, especially those deployed in advice-giving, healthcare, or social decision-making contexts where stigma harm is possible.
- 注意
- The benchmark reveals LLMs express stigma at 97.5% rates under group pressure, but it remains unclear how findings transfer to production systems or whether detection improvements are feasible.
- llm
- language model
- prompt
- eval
- benchmark
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