Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations

TL;DR

This paper introduces a large, novel dataset of personalized dialogues based on journal entries, capturing evolving human personalities and improving LLMs' ability to generate authentic, personality-rich conversations.

Contribution

The authors create a new dataset from Reddit journal entries, cluster and filter data by personality traits, and fine-tune LLMs to produce more authentic, personality-aligned dialogues.

Findings

11% improvement in personality trait accuracy

Enhanced coherence and personality richness in generated dialogues

Effective clustering and filtering of journal data

Abstract

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.