# LoSoNA：局部社交规范适应基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-12 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmqfd53o400drsl2axzfvs8rl
- 原文链接：https://arxiv.org/abs/2606.14600

## AI 摘要

LoSoNA 是一个评估大语言模型在多人群聊中推断并适应局部社交规范的基准。每个场景提供群聊记录，其中非目标参与者展示隐含规范，最后迫使目标模型回应以检测其是否推断出该规范。评估了八个前沿与开源模型，在四种提示条件下测试。朴素提示对多数模型效果有限；显式规范感知提示帮助不均，Gemini 3.1 Pro 达到 84.2%，Claude Fable 5 达到 81.6%，而其他模型提升较小甚至倒退。

## 正文

Online group chats are social spaces with local conversational norms that are rarely stated explicitly. The ability and willingness of LLM-based agents to recognize and adapt to these norms remains mostly unexplored. We introduce LoSoNA, a benchmark for local social norm adaptation in multi-party chat. Each scenario gives a subject model a curated group-chat transcript in which non-subject participants demonstrate a hidden local norm, followed by a final elicitor turn that forces a response revealing whether the subject has inferred that norm. We evaluate eight frontier and open-weight models under four prompting conditions that vary how explicitly the model is told to treat the prior conversation as evidence for how it should answer. Naive prompting remains limited for most models; explicit norm-aware prompting helps unevenly, with Gemini 3.1 Pro reaching 84.2% and Claude Fable 5 reaching 81.6%, while several other models show small gains or regressions. LoSoNA contributes to recent calls for evaluating LLM social capabilities by testing whether models can infer local conversational norms from precedent and use them in a one-turn group-chat response.