Official repository for InvestAlign: Align LLMs with Investor Decision-Making under Herd Behavior at NeurIPS 2024 Workshop AFM
Large Language Models (LLMs) can be leveraged to assist in solving complex investment problems. However, the investment decisions generated by existing LLMs often deviate from real-user data. One method to align LLMs with investor decision-making processes is Supervised Fine-Tuning (SFT), which requires a substantial amount of real-user data that is costly to collect and raises concerns about privacy and security. In this work, we propose InvestAlign, an efficient method that constructs large-scale SFT training datasets based on the theoretical solution to a similar and simpler optimal investment problem, rather than the original complex one. We theoretically demonstrate that fine-tuning LLMs with these datasets leads to faster parameter convergence compared to using real-user data. By fine-tuning LLMs, we obtain InvestAgents, which align more with real-user data than pre-SFT LLMs in both the simple and original complex problems. This highlights InvestAlign as a promising approach with the potential to address complex optimal investment problems and align LLMs with investor decision-making processes in economics and finance.
pip install -r requirements.txt
# Absolute herd behavior (P2)
bash scripts/infer_seq.sh
# Relative herd behavior (P1)
bash scripts/infer_seq_relative.sh