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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 26·5 min read
Can large language models assist choice modelling? Insights into prompting strategies and current models' capabilities
TL;DR
Large Language Models (LLMs) can assist in discrete choice modelling, particularly in specifying and estimating Multinomial Logit models. Structured prompting strategies enhance their performance, revealing both potential and limitations in their application.
✦ Why It Matters
Experiment with structured prompts in LLMs to enhance your choice modelling workflows today.
Key Takeaways
- Utilize structured prompts to improve LLM performance in choice modelling.
- Consider proprietary LLMs for better utility specification generation.
- Limit data access to enhance LLM reasoning capabilities.
- Explore LLMs' ability to estimate models through self-generated code.
How It Works
The study employs a systematic experimental framework to evaluate LLMs by varying modelling goals, prompting strategies, and data availability. By using structured prompts, particularly Chain-of-Thought, LLMs can better generate and estimate utility specifications for MNL models.