Conformal Information Pursuit for Interactively Guiding Large Language Models
Published 7 Nov 2025 · arXiv · Kwan Ho Ryan Chan
Overview
The paper presents Conformal Information Pursuit (C-IP) as a novel approach to guide Large Language Models (LLMs) in interactive question-answering tasks. C-IP aims to improve the efficiency of LLMs by using conformal prediction sets to estimate uncertainty, thereby enhancing predictive performance and reducing the number of queries needed.
Key Insights
- Conformal Information Pursuit (C-IP): C-IP uses conformal prediction sets to estimate uncertainty, providing a distribution-free and robust method compared to traditional conditional entropy.
- Performance Improvement: Experiments show that C-IP achieves better predictive performance and shorter query-answer chains than previous methods.
- Application in Medical Settings: In interactive medical consultations, C-IP offers competitive performance with single-turn predictions and greater interpretability.
BFSI Relevance
Why Relevant
The methodology can be applied to enhance customer service interactions in BFSI sectors, improving efficiency and customer satisfaction.
Primary Sector
Financial Services
Subsectors
- Customer Service
- Interactive Banking
Actionable Implications
- Implement C-IP to improve customer interaction efficiency.
- Use C-IP to enhance decision-making processes in interactive financial consultations.
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