arXiv AI
Sep 21

Do Personality-Tuned LLMs Make Better Social Agents?

The paper examines whether fine‑tuning large language models (LLMs) with personality‑labelled data improves their ability to act as socially interactive agents. Two small open‑weight LLMs were fine‑tuned on a corpus of personality‑labelled social media posts and dialogues, and the resulting models were evaluated in various social interaction scenarios by independent LLM judges. The findings show that the fine‑tuned models do not outperform their baseline counterparts in role‑playing personalities, though they offer comparable text quality and increased linguistic diversity for the Qwen models; low inter‑rater agreement limits confidence in the results, suggesting future work should focus on training data quality and domain alignment.

By Tim Krabbe, Xiaodan Shi
arXiv AI
Jul 21

Toward Anthropomorphic Dialogue: A Closed-Loop Framework for Human-Like Chat Generation, Evaluation, and Preference Alignment

arXiv:2607. 17191v1 Announce Type: new Abstract: Human-like private chat requires more than fluent response generation: a system must preserve persona, relationship, memory, bounded knowledge, medium-specific timing, and a coherent multi-turn arc.

By Wentao Liu, Siyu Song, Xi Chen, Youjia Li, Xiaokun Wang, Min Ji, Ji Wang
arXiv Computation and Language
Sep 14

Creating an Atomic User Model for Personality-Aware Large Language Model Interaction

The paper introduces the Atomic User Model (AUM), a structured representation of a user’s personality that separates a stable identity nucleus from four interpretable shells—psychological, cognitive & experiential, behavioural, and social—along with cross-shell entries for conflict and authenticity. It proposes using AUM as a retrieval index rather than a prompt prefix, enabling a task‑specific, budgeted retrieval of relevant fields at generation time. Experiments with simulated participants show that retrieving eight AUM fields improves style fidelity, preference accuracy, and user voice identification compared to flat preference notes, especially benefiting users whose default assistant performs poorly.

By B. Sankar, Deepthika S, Pawni Yadav, Amogh A S