mlsanityData to 7 Oct 2026

Augmenting Dialog with Think-Aloud Utterances for Modeling Individual Personality Traits by LLM

arXiv 2510.09158 ↗First posted 10 Oct 2025cs.CLcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

This study proposes augmenting dialog data with think-aloud utterances (TAUs) for modeling individual personalities in text chat by LLM. TAU is a verbalization of a speaker's thought before articulating the utterance. We expect "persona LLMs" trained with TAU-augmented data can mimic the speaker's personality trait better. We tested whether the trained persona LLMs obtain the human personality with respect to Big Five, a framework characterizing human personality traits from five aspects. The results showed that LLMs trained with TAU-augmented data more closely align to the speakers' Agreeableness and Neuroticism of Big Five than those trained with original dialog data. We also found that the quality of TAU-augmentation impacts persona LLM's performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 8 of its 28 references that are in the map. First posted on arXiv 10 Oct 2025.

Builds on

8 of 28 references.

References · 8

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  2. 2024Qwen2.5 Technical Reportcited by 4,713
  3. 2024GPT-4o System Cardcited by 4,467
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  5. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
  6. 2023ChatHaruhi: Reviving Anime Character in Reality via Large Language Modelcited by 57
  7. 2023Have Large Language Models Developed a Personality?: Applicability of Self-Assessment Tests in Measuring Personality in LLMscited by 32
  8. 2023LARP: Language-Agent Role Play for Open-World Gamescited by 24

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  1. 2024BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data
  2. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
  3. 2025Evaluating LLM Alignment on Personality Inference from Real-World Interview Data
  4. 2025PersonaFuse: A Personality Activation-Driven Framework for Enhancing Human-LLM Interactions
  5. 2026Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis
  6. 2024P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts
  7. 2025Can LLMs Infer Personality from Real World Conversations?
  8. 2026Do Personality-Tuned LLMs Make Better Social Agents?