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Updated: Jul 2, 2026
03:14
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Large language models accurately identify decision reasons in verbal reports
Kamil Fuławka1,2, Ralph Hertwig2, Dirk U Wulff2,3
1Center Synergy of Systems, Dresden University of Technology, Dresden 01069, Germany.
Summary
Large language models (LLMs) analyze verbal reports to reveal human decision-making reasons under risk. This approach offers more interpretable and accurate insights than choice data alone.
Area of Science:
- Cognitive Science
- Behavioral Economics
- Artificial Intelligence
Background:
- Decision science traditionally uses behavioral data, but faces limitations due to invariance assumptions.
- Understanding the rationale behind human choices under risk is crucial but challenging.
Purpose of the Study:
- To develop and validate a scalable framework using LLMs to analyze verbal reports for decision reasons.
- To challenge traditional invariance assumptions in decision science.
Main Methods:
- Utilized a validated large language model (LLM) to analyze free-text verbal reports of participants' choices between monetary lotteries.
- Compared reasons identified from verbal reports with choices alone.
Main Results:
- The LLM accurately identified predefined decision reasons in 95% of trials, aligning with choices.
- Decision reasons varied systematically, driven by choice problem structure rather than individual differences.
- Verbal report analysis provided more parsimonious and informative decision representations.
Conclusions:
- Verbal reports are a rich data source for understanding decision-making processes.
- The LLM framework unlocks the potential of verbal data for more context-sensitive and interpretable decision models.
- Findings challenge foundational invariance assumptions in decision science.
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