Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs (2501.00555v1)

Abstract: LLMs are empowering decision-making in several applications, including tool or API usage and answering multiple-choice questions (MCQs). However, they often make overconfident, incorrect predictions, which can be risky in high-stakes settings like healthcare and finance. To mitigate these risks, recent works have used conformal prediction (CP), a model-agnostic framework for distribution-free uncertainty quantification. CP transforms a \emph{score function} into prediction sets that contain the true answer with high probability. While CP provides this coverage guarantee for arbitrary scores, the score quality significantly impacts prediction set sizes. Prior works have relied on LLM logits or other heuristic scores, lacking quality guarantees. We address this limitation by introducing CP-OPT, an optimization framework to learn scores that minimize set sizes while maintaining coverage. Furthermore, inspired by the Monty Hall problem, we extend CP's utility beyond uncertainty quantification to improve accuracy. We propose \emph{conformal revision of questions} (CROQ) to revise the problem by narrowing down the available choices to those in the prediction set. The coverage guarantee of CP ensures that the correct choice is in the revised question prompt with high probability, while the smaller number of choices increases the LLM's chances of answering it correctly. Experiments on MMLU, ToolAlpaca, and TruthfulQA datasets with Gemma-2, Llama-3 and Phi-3 models show that CP-OPT significantly reduces set sizes while maintaining coverage, and CROQ improves accuracy over the standard inference, especially when paired with CP-OPT scores. Together, CP-OPT and CROQ offer a robust framework for improving both the safety and accuracy of LLM-driven decision-making.

Optimizing Uncertainty and Decision-Making in LLMs Using Conformal Prediction

The paper "Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs" presents a paper focusing on enhancing decision-making capabilities of LLMs by utilizing a refined conformal prediction (CP) framework. LLMs, such as Gemini-2, Llama-3, and Phi-3, leverage machine learning for decision-making tasks like multiple-choice question answering (MCQ) and tool usage. However, a common problem with LLMs is their tendency to provide overconfident yet incorrect predictions. This presents a significant challenge, especially in domains with high failure costs like healthcare and finance.

To mitigate these risks, the authors propose CP-OPT, a novel framework aimed at optimizing conformal scores to reduce prediction set sizes, thus managing uncertainty more effectively. Additionally, they introduce the conformal revision of questions (CROQ), inspired by the Monty Hall problem, which fine-tunes the inquiry to encompass only options within the CP-generated prediction set. This approach is pivotal because reducing the number of choices inherently boosts the probability of the LLM selecting the correct answer due to the coverage guarantees provided by CP.

Contribution to the Field

1. CP-OPT Framework: The paper introduces an innovative score optimization framework that refines conformal prediction by minimizing prediction set sizes without compromising on the coverage. Unlike previous methodologies relying heavily on LLM's logit scores or heuristic-based approaches, CP-OPT ensures principled learning of conformal scores applicable to any pretrained LLM. Empirical validation exhibited a significant reduction in set sizes while maintaining the desired level of coverage.
2. CROQ Strategy: This method advocates for revising questions by narrowing down to choices within CP prediction sets, refining input to LLMs. Through empirical evaluation, CROQ demonstrated an appreciable improvement in accuracy compared to standard inference processes, with even greater enhancements when coupled with CP-OPT scores as opposed to conventional logit scores.

Experimental Results

The paper details experiments conducted across three datasets: MMLU, ToolAlpaca, and TruthfulQA, deploying various models like Gemma-2, Llama-3, and Phi-3. Results demonstrated that:

• Using CP-OPT, average conformal set sizes were reduced while sustaining up to 95% coverage.
• CROQ yielded improvements in decision accuracy over a wide spectrum of contexts.
• When combined, CP-OPT and CROQ provide a robust framework for handling uncertainty and improving decision accuracy in LLM-driven tasks.

Theoretical and Practical Implications

The work not only underscores theoretical advancements in optimizing conformal prediction for improved uncertainty quantification but also suggests practical ramifications in safety-critical applications deploying LLMs. By offering an effective mechanism to manage LLM uncertainty, the methodologies delineated in this paper have the potential to improve human-AI collaboration—specifically in scenarios where LLMs might defer uncertain decisions to human experts, fundamentally enhancing system reliability and trustworthiness.

Future Directions

The paper hints at potential extensions such as multi-rounds of CROQ, which might enhance accuracy further by repeatedly reducing response options iteratively. Additionally, experimentation with varying coverage levels can provide insights into optimal parameter selection under different conditions. Exploring the translation of these techniques to diverse domains (e.g., dynamic tool usage scenarios) could provide further validation and refinement of the methodologies.

In summary, this paper provides substantial value to the field of machine learning by addressing the specific issue of uncertainty quantification and decision-making accuracy in LLMs through innovative adaptations of conformal prediction techniques, thus paving the way for more robust and reliable AI systems.