Conformal Information Pursuit for Interactively Guiding Large Language Models
Conformal Information Pursuit (C-IP) leverages prediction set sizes to estimate uncertainty, optimizing multi-round queries for large language models, improving efficiency and interpretability.
Key Findings
Methodology
This work extends the information pursuit (IP) framework by integrating conformal prediction techniques. Instead of estimating mutual information directly, C-IP uses the average size of conformal prediction sets to upper bound conditional entropy, enabling distribution-free, robust uncertainty estimation. The approach involves constructing prediction sets with coverage guarantees, then greedily selecting queries that minimize expected set size. Two sampling strategies—uniform over query sets and LLM-based simulation—are employed to generate query histories. The algorithm iteratively refines query selection, ensuring theoretical guarantees on coverage and informativeness, validated through experiments on 20 Questions and medical datasets.
Key Results
- In 20 Questions, C-IP shortened query chains by over 20%, achieving 85% accuracy versus 75% baseline. It outperformed traditional IP and uncertainty-based methods in query efficiency.
- On MediQ medical data, C-IP maintained 90% coverage with fewer interaction rounds, matching single-turn prediction performance and providing interpretability.
- Across scenarios, C-IP's uncertainty bounds based on prediction set sizes were more stable and informative than entropy estimates, leading to improved decision-making.
Significance
This research addresses the core challenge of reliable uncertainty estimation in large language models during multi-turn interactions. By replacing probability-based measures with conformal prediction sets, it offers a distribution-free, theoretically guaranteed method that enhances robustness, interpretability, and efficiency. The demonstrated success in both synthetic and real-world medical tasks highlights its potential to transform interactive AI systems, enabling more reliable, transparent, and resource-efficient decision processes in applications like diagnostic assistance, customer service, and educational tools.
Technical Contribution
The paper introduces a novel integration of conformal prediction with the IP framework, providing a distribution-free upper bound on conditional entropy via prediction set sizes. It develops a greedy query selection algorithm (C-IP) that minimizes these bounds, supported by theoretical guarantees on coverage and informativeness. The work also proposes practical sampling strategies—uniform and LLM-based—to generate query histories, ensuring scalability and adaptability. This approach fundamentally shifts the paradigm from probability calibration to set-based uncertainty quantification, broadening the applicability of information-theoretic methods in large language model interaction.
Novelty
This is the first work to utilize conformal prediction set sizes as a surrogate for uncertainty in sequential query strategies for large models. Unlike prior methods relying solely on probabilistic calibration, C-IP leverages distribution-free, finite-sample guarantees, offering a new theoretical and practical framework for robust, interpretable multi-round querying. Its innovative combination of conformal inference and information pursuit sets a new direction in AI interpretability and efficiency.
Limitations
- The method's reliance on accurate prediction set calibration may be challenged in high-noise or highly imbalanced data scenarios.
- Sampling strategies, especially LLM simulation, can be computationally intensive in large query spaces.
- Extension to multi-modal or highly complex tasks requires further validation, as current experiments focus on text-based QA.
Future Work
Future research will focus on improving prediction set calibration in diverse environments, integrating adaptive sampling techniques, and extending the framework to multi-modal data. Additionally, exploring error control mechanisms and real-time deployment in clinical and customer service settings will be key to broadening practical impact. Theoretical work on bounds and convergence in more complex scenarios also remains an open avenue.
AI Executive Summary
In the rapidly evolving field of natural language processing, large language models (LLMs) have demonstrated remarkable capabilities in generating human-like responses. However, their performance in multi-turn, interactive question-answering remains limited by challenges in uncertainty estimation and query efficiency. Traditional approaches rely heavily on probabilistic calibration techniques, which often suffer from overconfidence or miscalibration, leading to suboptimal query selection and increased interaction length.
This paper introduces Conformal Information Pursuit (C-IP), a novel framework that leverages conformal prediction sets to estimate the uncertainty of LLM outputs robustly and distribution-independently. Instead of directly estimating mutual information or conditional entropy—tasks that are complicated by probability miscalibration—C-IP uses the average size of conformal prediction sets as a surrogate measure. This approach provides a guaranteed coverage, ensuring that the true answer lies within the prediction set with high probability, regardless of the underlying distribution.
The core idea is to formulate a greedy query selection strategy that minimizes the expected size of these prediction sets at each step. This effectively maximizes information gain while maintaining robustness against probability calibration errors. The algorithm employs two sampling strategies: uniform over a predefined query set and simulation via LLM prompting, making it adaptable to various application scenarios. Extensive experiments on 20 Questions and medical diagnosis datasets demonstrate that C-IP significantly shortens query chains—reducing interaction steps by over 20%—while maintaining high accuracy and coverage. In medical diagnosis, it achieves comparable performance to single-turn prediction, with enhanced interpretability.
The significance of this work lies in its ability to provide a distribution-free, theoretically guaranteed method for uncertainty estimation in multi-round interactions. By shifting from probability-based measures to set-based metrics, it addresses fundamental limitations of existing techniques, paving the way for more reliable, transparent, and efficient AI systems. Future directions include refining calibration methods, scaling to multi-modal data, and deploying in real-world clinical and customer service environments, promising a new era of robust human-AI collaboration.
Deep Analysis
Background
近年来，预训练大模型（如GPT、BERT）在自然语言处理中的表现持续提升，但在多轮交互中的信息利用和不确定性估计仍存在瓶颈。传统方法依赖概率校准（如温度缩放、Platt缩放）改善输出概率，但在多轮场景中，模型的过度自信和偏差严重影响信息利用效率。信息追踪（IP）框架通过最大化互信息指导查询选择，但在实际中难以精确估算互信息，尤其在模型概率偏差明显时效果受限。预测集（conformal prediction）作为一种无分布依赖的统计工具，近年来被用于模型校准和不确定性估算，具有较强的鲁棒性和解释性。结合二者，本文旨在解决多轮交互中不确定性估计不足的问题，推动大模型在实际应用中的表现。
Core Problem
核心问题在于如何在多轮交互中准确估算模型的不确定性，从而指导下一轮的最优查询。现有方法多依赖概率分布的校准，但模型的概率偏差导致互信息估算失准，影响交互效率和预测准确性。尤其在医疗诊断、自动问答等场景中，错误的估算会带来严重后果。如何利用无分布假设的统计工具，提供稳健、可靠的不确定性指标，成为亟待解决的关键难题。
Innovation
本研究的创新点包括：1）引入预测集大小作为不确定性指标，避免概率偏差带来的影响；2）结合信息追踪策略，设计贪心算法（C-IP）以最小化预测集大小，最大化信息获取；3）提出两种采样策略（均匀采样和LLM模拟）以适应不同场景；4）提供理论保证，确保预测集覆盖率和信息最大化。这些创新共同推动多轮交互中不确定性估计的鲁棒性和效率，为大模型交互策略提供新思路。
Methodology
- �� 构建预测集：利用conformal prediction保证在不同分布下的覆盖率，通过校准数据获得预测集，计算其平均大小作为不确定性指标。
- �� 查询选择：在每轮中，选择使预测集大小最小的查询，等价于最大化信息增益，具体通过优化预测集大小的上界实现。
- �� 采样策略：采用均匀采样和LLM模拟两种方式生成查询历史，确保算法的多样性和适应性。
- �� 迭代优化：每轮根据预测集大小选择最优查询，直到预测集收敛或达到最大轮次。
- �� 理论保证：通过预测集的覆盖率保证，确保多轮交互中的不确定性估算具有统计学意义，提升模型鲁棒性。
Experiments
在20 Questions任务中，使用动物属性数据集（AwA2）验证C-IP的性能。比较基线包括随机选择、传统互信息估算和链式推理。评估指标涵盖查询链长度、预测准确率和覆盖率。医疗场景中，采用MediQ数据集，验证模型在多轮交互中的表现。超参数包括目标覆盖率（90%）和样本数（4次采样）。通过模拟和真实交互，验证算法在不同场景下的鲁棒性和效率。
Results
C-IP在20 Questions中，缩短查询链超过20%，准确率达85%，优于传统IP的75%。在医疗问答中，保持90%覆盖率，交互轮数明显减少，表现与单轮预测相当。实验还显示，基于预测集大小的估算比传统概率方法更稳定，能有效避免模型过度自信带来的偏差，提升整体性能。
Applications
该方法适用于多轮人机交互、智能诊断、自动问答等场景。只需定义查询空间或利用模型生成查询，即可实现高效信息获取。特别适合医疗、客服、教育等行业，能在保证可靠性的同时，减少交互轮次，提高用户体验。
Limitations & Outlook
当前方法依赖预测集的准确校准，在极端偏态或高噪声环境下可能表现不佳。采样策略在大规模查询空间中效率有限，未来需优化。此外，模型在多模态或复杂任务中的适应性仍待验证，存在一定局限。
Plain Language Accessible to non-experts
想象你在找一本藏在书架上的书。你可以每次问一个问题，比如“这本书在左边吗？”或者“它是小说吗？”每个问题都能帮你缩小范围，但你不知道哪个问题能最快帮你找到答案。传统方法就像随便问，可能问了很多次都没找到。而C-IP就像有个聪明的助手，他知道每个问题能帮你省多少时间，帮你更快找到书。这是通过一种特殊的数学技巧，确保每次问的问题都最有效，减少不必要的麻烦。
ELI14 Explained like you're 14
想象你在玩猜谜游戏，你需要问对问题才能最快猜出答案。普通的方法就像随便问问题，有时候问了很多次还猜不准。而C-IP就像一个聪明的朋友，他知道每个问题能帮你省多少时间，所以每次都问最关键的问题。它用一种特别的数学技巧，确保每次问的问题都能带来最大帮助。这样，你就能用更少的问话，快速猜到答案。这个方法在电脑和机器人里也能用，让它们变得更聪明、更快，特别是在像医疗诊断或客服聊天这样的场景中。
Abstract
A significant use case of instruction-finetuned Large Language Models (LLMs) is to solve question-answering tasks interactively. In this setting, an LLM agent is tasked with making a prediction by sequentially querying relevant information from the user, as opposed to a single-turn conversation. This paper explores sequential querying strategies that aim to minimize the expected number of queries. One such strategy is Information Pursuit (IP), a greedy algorithm that at each iteration selects the query that maximizes information gain or equivalently minimizes uncertainty. However, obtaining accurate estimates of mutual information or conditional entropy for LLMs is very difficult in practice due to over- or under-confident LLM proba- bilities, which leads to suboptimal query selection and predictive performance. To better estimate the uncertainty at each iteration, we propose Conformal Information Pursuit (C-IP), an alternative approach to sequential information gain based on conformal prediction sets. More specifically, C-IP leverages a relationship between prediction sets and conditional entropy at each iteration to estimate uncertainty based on the average size of conformal prediction sets. In contrast to conditional entropy, we find that conformal prediction sets are a distribution-free and robust method of measuring uncertainty. Experiments with 20 Questions show that C-IP obtains better predictive performance and shorter query-answer chains compared to previous approaches to IP and uncertainty-based chain-of-thought methods. Furthermore, extending to an interactive medical setting between a doctor and a patient on the MediQ dataset, C-IP achieves competitive performance with direct single-turn prediction while offering greater interpretability.