With the growing popularity of dialogue agents based on large language models
(LLMs), urgent attention has been drawn to finding ways to ensure their
behaviour is ethical and appropriate. These are largely interpreted in terms of
the 'HHH' criteria: making outputs more helpful and honest, and avoiding
harmful (biased, toxic, or inaccurate) statements. Whilst this semantic focus
is useful from the perspective of viewing LLM agents as mere mediums for
information, it fails to account for pragmatic factors that can make the same
utterance seem more or less offensive or tactless in different social
situations. We propose an approach to ethics that is more centred on relational
and situational factors, exploring what it means for a system, as a social
actor, to treat an individual respectfully in a (series of) interaction(s). Our
work anticipates a set of largely unexplored risks at the level of situated
interaction, and offers practical suggestions to help LLM technologies behave
as 'good' social actors and treat people respectfully.

LLM 技术在社交互动中的伦理行为与尊重个体的关系和情境因素相关，研究提供了有关 LLM 技术作为社会行为者的行为规范和尊重人的实际建议。

评估与语言机器人互动的标准：以尊重为视角

What makes for a 'good' social actor? Using respect as a lens to  evaluate interactions with language agents

Many news comment mining studies are based on the assumption that comment is
explicitly linked to the corresponding news. In this paper, we observed that
users' comments are also heavily influenced by their individual characteristics
embodied by the interaction history. Therefore, we position to understand news
comment behavior by considering both the dispositional factors from news
interaction history, and the situational factors from corresponding news. A
three-part encoder-decoder framework is proposed to model the generative
process of news comment. The resultant dispositional and situational
attribution contributes to understanding user focus and opinions, which are
validated in applications of reader-aware news summarization and news
aspect-opinion forecasting.

考虑了用户的个体特征和互动历史，该研究通过提出一个三部分的编码器 - 解码器框架来模拟新闻评论生成过程，并以验证读者感知新闻摘要和新闻方面 - 观点预测的应用为基础，从而为理解用户焦点和意见做出了贡献。