The content discusses using large language models (LLMs) to model the beliefs, preferences, and behaviors of a specific human population. This can be useful for applications like conducting simulated focus groups, virtual surveys, and testing behavioral interventions that would be expensive, impractical, or unethical to conduct with real human participants.
The authors benchmark and evaluate two fine-tuning approaches using an existing survey dataset on preferences for battery electric vehicles (BEVs). They evaluate the models' ability to match population-wide statistics as well as individual responses, and investigate the role of temperature in controlling the trade-off between these two metrics.
Additionally, the authors propose and evaluate a novel loss term to improve model performance on survey questions that require a numeric response. The results indicate that fine-tuning can reduce both population-level and individual-level error metrics compared to pre-trained models, and that larger models tend to perform better. The authors also find that quantization techniques like QLoRA provide significant computational savings with minimal degradation in performance.
Overall, the work demonstrates the potential of using LLMs as statistical proxies for studying human preferences and behaviors, while also highlighting the challenges in accurately modeling individual-level responses.
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by Keiichi Nami... at arxiv.org 04-01-2024
https://arxiv.org/pdf/2403.20252.pdfDeeper Inquiries