HumanLLM: Towards Personalized Understanding and Simulation of Human Nature

TL;DR

HumanLLM introduces a foundation model specialized for personalized understanding and simulation of human cognition and behavior by building the Cognitive Genome Dataset from Reddit, Twitter, Blogger, and Amazon. A rigorous three-stage pipeline—data filtering, data synthesis, and data quality control—produces millions of user logs distilled into User, Scenario, and Social QA components, grounded by Lewin's $B = f(P, E)$ principle. Six training tasks and a model-merging strategy yield a family of HumanLLM models that outperform baselines on in-domain tasks and show strong generalization to out-of-domain social intelligence benchmarks, while real-world applications demonstrate improved data generation, explanation, and personalized writing. The work highlights the potential of real-world, user-centric data to advance human-centric AI and social simulation, with implications for research and customer-centric applications.

Abstract

Motivated by the remarkable progress of large language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human behavior--a capability with profound implications for transforming social science research and customer-centric business insights. However, LLMs often lack a nuanced understanding of human cognition and behavior, limiting their effectiveness in social simulation and personalized applications. We posit that this limitation stems from a fundamental misalignment: standard LLM pretraining on vast, uncontextualized web data does not capture the continuous, situated context of an individual's decisions, thoughts, and behaviors over time. To bridge this gap, we introduce HumanLLM, a foundation model designed for personalized understanding and simulation of individuals. We first construct the Cognitive Genome Dataset, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon. Through a rigorous, multi-stage pipeline involving data filtering, synthesis, and quality control, we automatically extract over 5.5 million user logs to distill rich profiles, behaviors, and thinking patterns. We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences. Comprehensive evaluations demonstrate that HumanLLM achieves superior performance in predicting user actions and inner thoughts, more accurately mimics user writing styles and preferences, and generates more authentic user profiles compared to base models. Furthermore, HumanLLM shows significant gains on out-of-domain social intelligence benchmarks, indicating enhanced generalization.

Figures (5)

• Figure 1: A graphical illustration for our pipeline.
• Figure 2: Performance comparison between HumanLLM and its base model across three applications. Full results can be found in Appendix \ref{['appendix:gpt results']}.
• Figure 3: Case Study for Human Explainer Application.
• Figure B1: Case Study for Profile Generation Application.
• Figure B2: Case Study for Personalized Writing Application.