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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Can language representation models think in bets?

Zhisheng Tang1, Mayank Kejriwal1

  • 1Information Sciences Institute, USC Viterbi School of Engineering, 4676 Admiralty Way 1001, Marina Del Rey, CA 90292 USA.

Royal Society Open Science
|March 31, 2023
PubMed
Summary

Language representation models (LRMs) can make rational decisions when fine-tuned on structured bet questions. Performance significantly drops with structural changes, indicating sensitivity to question format in decision-making tasks.

Keywords:
cognitive sciencedecision-making problemslanguage representation modelsneural language modelspreference elicitationtransformer neural networks

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Natural Language Processing

Background:

  • Transformer-based language representation models (LRMs) excel at natural language understanding tasks.
  • Evaluating the rational decision-making capabilities of LRMs is crucial for real-world applications.

Purpose of the Study:

  • To investigate the rational decision-making ability of LRMs.
  • To assess LRMs' capacity to choose outcomes with optimal or positive expected gain, modeling decisions as bets.

Main Methods:

  • Developed decision-making benchmarks inspired by cognitive science, framing problems as bets.
  • Conducted experiments on four established LRMs, fine-tuning them on bet questions with identical structures.
  • Analyzed performance variations based on bet question structure and expected gain of outcomes.

Main Results:

  • LRMs demonstrate 'thinking in bets' ability when fine-tuned on identically structured bet questions.
  • Modifying bet question structure decreased LRM performance by over 25% on average, though performance remained above random.
  • LRMs were more rational in selecting outcomes with non-negative expected gain compared to optimal or strictly positive expected gain.

Conclusions:

  • LRMs show potential for cognitive decision-making tasks but require further research for robust rational decision-making.
  • Fine-tuning with consistent question structures is key to enhancing LRM decision-making performance.
  • LRMs exhibit a preference for non-negative expected gains, suggesting a specific decision-making bias.