Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
Published:Dec 24, 2025 05:00
•1 min read
•ArXiv AI
Analysis
This paper introduces an innovative approach called "interpolative decoding" to control and modulate personality traits in large language models (LLMs). By using pairs of opposed prompts and an interpolation parameter, the researchers demonstrate the ability to reliably adjust scores along the Big Five personality dimensions. The study's strength lies in its application to economic games, where LLMs mimic human decision-making behavior, replicating findings from psychological research. The potential to "twin" human players in collaborative games by systematically searching for interpolation parameters is particularly intriguing. However, the paper would benefit from a more detailed discussion of the limitations of this approach, such as the potential for biases in the prompts and the generalizability of the findings to more complex scenarios.
Key Takeaways
- •Interpolative decoding allows for controlled modulation of personality traits in LLMs.
- •LLMs can mimic human decision-making behavior in economic games using this technique.
- •The method shows potential for "twinning" human players in collaborative games.
Reference
“We leverage interpolative decoding, representing each dimension of personality as a pair of opposed prompts and employing an interpolation parameter to simulate behavior along the dimension.”