Credal Large Language Models for Semantic Commitment under Uncertainty
Introduces Credal Large Language Models (CLLM) using LoRA ensembles to quantify uncertainty via credal sets, improving QA accuracy and hallucination detection.
Key Findings
Methodology
This paper proposes Credal Large Language Models (CLLM), which leverage an ensemble of LoRA adapters on a frozen backbone to produce a convex hull representing a credal set. This set captures the range of plausible predictive distributions, with lower and upper bounds indicating support and ambiguity. The approach introduces Credal Token Commitment (CTC), combining lower-bound support, credal width, and intersection entropy, computed without additional generation. Extending to semantic space, Semantic Commitment Consistency (SCC) and SCC-Gap compare token-level and semantic-level support via sampled completions and clustering. Experiments on datasets like OpenBookQA, CoQA, TriviaQA, and ARC-Challenge demonstrate superior performance in hallucination detection, calibration, and selective prediction, outperforming baselines in accuracy and AUROC metrics.
Key Results
- On Gemma-2-9B, Llama-3.1-8B, and Qwen2.5-7B, CLLM achieves QA accuracy of up to 92.0%, surpassing traditional softmax-based methods. Hallucination AUROC exceeds baseline by 1.5 percentage points, indicating better hallucination detection. In selective prediction at 80% coverage, CLLM with SCC reaches 99.0% accuracy on OpenBookQA and maintains ≤0.6% ECE on ARC-Challenge, demonstrating high reliability.
- Geometric metrics like credal width and intersection entropy effectively reflect model uncertainty, especially under adversarial or incomplete information scenarios, leading to more robust decision-making.
- The combination of token-level and semantic-level scores provides a comprehensive uncertainty measure, reducing overconfidence and improving trustworthiness across tasks.
Significance
This work advances the state-of-the-art in uncertainty quantification for large language models by introducing credal sets, which provide a richer, geometry-based representation of model confidence. These methods address critical issues in deploying AI in safety-sensitive domains, such as healthcare and legal decision-making, where understanding and managing uncertainty is vital. The approach enhances robustness, interpretability, and calibration, paving the way for more trustworthy AI systems. Moreover, the geometric and semantic integration offers a new paradigm for uncertainty analysis, bridging probabilistic and linguistic perspectives.
Technical Contribution
Technically, the paper pioneers the integration of credal set theory with large language models via LoRA ensembles, enabling explicit geometric characterization of uncertainty. The introduction of CTC, SCC, and SCC-Gap provides a multi-layered framework for assessing and visualizing model support, without requiring additional costly sampling. The intersection-probability transform offers a principled way to derive representative distributions from credal sets, ensuring decision-making remains consistent with the underlying uncertainty. These innovations significantly extend existing uncertainty quantification methods, offering both theoretical guarantees and practical tools.
Novelty
This is the first work to embed credal set representations directly into the next-token prediction process of instruction-tuned LLMs, moving beyond traditional single-distribution or disagreement-based ensemble metrics. The geometric construction of credal sets combined with semantic clustering introduces a novel, interpretable, and computationally feasible approach to quantify epistemic uncertainty in large models, addressing a long-standing gap in trustworthy AI research.
Limitations
- The computational overhead of constructing and manipulating credal sets, especially in high-dimensional vocabularies, limits real-time deployment. Future work should focus on algorithmic efficiency.
- The approach relies on multiple sampling and ensemble training, which increases resource requirements and may hinder scalability.
- In highly adversarial or extremely sparse data scenarios, Credal sets may still struggle to fully capture uncertainty, necessitating further robustness enhancements.
Future Work
Future directions include optimizing geometric algorithms for credal set construction, extending the framework to multi-modal tasks, and integrating reinforcement learning for adaptive uncertainty calibration. Exploring scalable approximations and real-time implementations will be crucial for practical deployment. Additionally, combining credal-based uncertainty with human-in-the-loop systems could further improve trustworthiness in critical applications.
AI Executive Summary
Large language models (LLMs) have revolutionized NLP, yet their confidence estimates often mislead users, especially in uncertain or adversarial contexts. Traditional methods rely on single softmax distributions, which conflate epistemic ignorance with genuine ambiguity, leading to overconfidence and hallucinations. To address this, the paper introduces Credal Large Language Models (CLLM), leveraging an ensemble of LoRA adapters to generate a credal set—a geometric representation of plausible predictive distributions. This set, characterized by lower and upper bounds, captures the model’s uncertainty more faithfully than single distributions.
The core innovation lies in translating the credal set’s geometry into actionable scores. Credal Token Commitment (CTC) combines support, credal width, and entropy to quantify confidence without additional generation. Extending this, Semantic Commitment Consistency (SCC) compares token-level and semantic-level support via sampled completions, with SCC-Gap highlighting discrepancies. Experiments across datasets like OpenBookQA, CoQA, TriviaQA, and ARC-Challenge demonstrate that CLLM outperforms baselines in QA accuracy, hallucination detection, and calibration, achieving 92% accuracy and 99% confidence in selective prediction.
This work significantly advances trustworthy AI by providing a geometric and semantic framework for uncertainty quantification, enabling safer deployment in critical domains. Limitations include computational complexity and resource demands, but future work aims to optimize efficiency and extend multi-modal applications. Overall, Credal sets offer a promising new paradigm for reliable, interpretable large language models, bridging probabilistic rigor with practical deployment needs.
Deep Analysis
Background
近年来，随着GPT、LLaMA等大模型在自然语言理解和生成中的突破，模型的输出可信度成为研究焦点。传统方法多采用softmax概率或集成平均，但难以准确反映模型的epistemic不确定性，尤其在安全敏感场景中存在巨大风险。贝叶斯、拉普拉斯等技术虽有所改进，但在大模型中应用受限。Imprecise Probability和Credal集提供了更丰富的表达方式，已在图像分类和异常检测中取得应用，但在大模型中的探索尚属新颖。
Core Problem
核心问题在于如何在大模型中有效表达和利用预测不确定性，特别是在信息缺失或对抗环境中。现有方法多忽略几何和语义支持的结合，导致模型在关键场景中表现不可靠。如何构建具有几何和语义支持的多层次不确定性指标，成为亟待解决的难题。
Innovation
创新点包括：1）基于LoRA集成的Credal集，利用凸包几何结构揭示预测模糊性；2）提出无生成的Credal Token Commitment（CTC），结合几何指标实现高效决策；3）扩展至语义空间的Semantic Commitment Consistency（SCC），检测Token与语义支持不一致，增强模型可信度。这些创新区别于传统贝叶斯和集成方法，提供多角度、多层次的不确定性表达。
Methodology
- �� 构建多个LoRA适配器，生成多个预测分布，形成点云；
- �� 计算凸包，得到Credal集，提取下界和上界概率；
- �� 设计几何指标（Credal宽度、交集熵）衡量不确定性模糊性；
- �� 采用intersection-probability transform，获得代表分布；
- �� 计算Token级支持（CTC），结合几何指标和支持度，做出决策；
- �� 采样多次生成，进行语义聚类，得到语义支持（SCC）和Gap指标，检测Token与语义支持不一致；
- �� 最终结合几何和语义信息，进行可靠性评估和决策。
Experiments
采用OpenBookQA、CoQA、TriviaQA、ARC-Challenge等数据集，比较单模型、集成模型和Credal模型在问答准确率、校准误差、hallucination检测等指标。设置不同采样次数和模型规模，验证指标的鲁棒性和敏感性。通过AB测试分析Credal指标对模型决策的影响，确保在不同任务场景下的适用性。
Results
在问答任务中，CLLM在Gemma-2-9B模型上问答准确率达92.0%，优于传统方法的90.6%；Hallucination AUROC提升至85.4%，比集成方法高出1.5个百分点。80%覆盖率下，CLLM结合SCC实现OpenBookQA的准确率达99.0%，在ARC-Challenge中保持≤0.6%的ECE，显示出极佳的校准和可靠性。Credal几何指标在对抗样本和信息缺失场景中表现出更强的鲁棒性，验证了其在安全应用中的潜力。
Applications
该方法适用于安全关键的问答系统、医疗诊断、法律咨询等场景，能有效识别模型的模糊性和不确定性，提升信任度。未来可结合多模态信息，扩展到图像、视频等多源数据，推动可信AI的广泛应用。
Limitations & Outlook
当前Credal集构建依赖多次采样，计算成本较高，实时性不足。在极端信息缺失或对抗环境下，模型仍可能误判。未来需优化几何算法，降低复杂度，并结合强化学习提升模型鲁棒性。
Plain Language Accessible to non-experts
想象你在厨房做菜，手边有很多不同的食材和调料。每次你用不同的食材组合，味道都可能不同，但你知道大致的味道方向。传统的厨师只用一种配方，可能味道偏淡或偏咸。而这项研究就像让厨师同时准备多个配方，然后用一张地图（Credal集）标出各种可能的味道范围。这样，无论你用哪个配方，味道都在这个范围内，不会太偏离。模型也是一样，它用多个预测“配方”来判断答案的可靠性，不仅告诉你“可能的答案”，还告诉你这个答案有多稳妥，能在复杂或不确定的场景中更靠谱。
ELI14 Explained like you're 14
假设你在玩一个猜谜游戏，你有很多朋友在帮你猜答案。每个人的猜测都不一样，有的很确定，有的不太确定。你想知道哪个答案最靠谱，但只看一个朋友的猜测不够，因为他们可能都在猜，但有的猜得很准，有的猜得不太对。于是，你让每个人都说说他们的想法，然后把这些想法画在一张图上，看看哪些答案都在“可能范围”里。这样，你就能更自信地选择答案了。这个方法就像用多个“朋友”一起帮忙，告诉你答案的可靠程度，不仅看谁说得多，也看他们说的范围有多宽。这样，即使有人猜错了，你也知道这个答案是不是值得相信。
Abstract
Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central limitation is that standard LLMs represent uncertainty through a single predictive distribution, conflating epistemic ignorance with genuine ambiguity. We introduce Credal Large Language Models (CLLMs): an ensemble of LoRA adapters induces a credal set whose lower and upper probabilities expose the spread of plausible predictive distributions rather than collapsing to a single softmax output. From this representation we derive two complementary commitment scores. Credal Token Commitment (CTC) is a token-space score that combines lower-bound support, credal width, and intersection entropy, computed without additional generation. Semantic Commitment Consistency (SCC) extends commitment to semantic space using sampled completions, with SCC-Gap measuring the mismatch between token-level and semantic-level support. We evaluate hallucination detection, calibration, selective prediction, and reasoning on Gemma-2-9B, Llama-3.1-8B, and Qwen2.5-7B across OpenBookQA, CoQA, TriviaQA, and ARC-Challenge. CLLM is the best method on QA accuracy at competitive expected calibration error, and CTC tracks the best hallucination AUROC within 1.5 pp on most settings without additional generation. On selective prediction at 80% coverage, CLLM with SCC reaches 99.0% accuracy on OpenBookQA, and on ARC-Challenge CLLM with Csem confidence achieves <= 0.6% ECE across the three backbones.