超越离散人格：通过日记密集型对话进行人格建模
Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations
AI总结：
针对静态人格数据集的局限，提出基于Reddit日记聚类与过滤的约40万对话数据集，利用Llama 3 70B生成人格丰富对话，微调后人格捕捉平均提升11%。
摘要：
大型语言模型（LLMs）显著提升了个性化对话能力。然而，现有的数据集如Persona Chat、Synthetic Persona Chat和Blended Skill Talk依赖于静态的、预定义的人格。这种方法往往导致对话无法捕捉人类人格的流动性和演变性。为了克服这些限制，我们引入了一个包含约400,000个对话的新数据集和一个使用Reddit上的长篇日记条目生成个性化对话的框架。我们的方法对每位作者的日记条目进行聚类，并通过选择最具代表性的聚类来过滤，确保保留的条目最能反映作者的人格。我们通过捕捉大五人格特质——开放性、尽责性、外向性、宜人性和神经质——进一步细化数据，确保对话真实反映个体的个性。使用Llama 3 70B，我们基于这些日记条目生成高质量、人格丰富的对话。在此数据集上微调模型，在捕捉人格特质方面平均提高了11%，在生成更连贯和人格驱动的对话方面优于现有方法。
英文摘要：
Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid and evolving nature. To overcome these limitations, we introduce a novel dataset with around 400,000 dialogues and a framework for generating personalized conversations using long-form journal entries from Reddit. Our approach clusters journal entries for each author and filters them by selecting the most representative cluster, ensuring that the retained entries best reflect the author's personality. We further refine the data by capturing the Big Five personality traits --openness, conscientiousness, extraversion, agreeableness, and neuroticism --ensuring that dialogues authentically reflect an individual's personality. Using Llama 3 70B, we generate high-quality, personality-rich dialogues grounded in these journal entries. Fine-tuning models on this dataset leads to an 11% improvement in capturing personality traits on average, outperforming existing approaches in generating more coherent and personality-driven dialogues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
