Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks (2511.03166v1)

Abstract: LLMs have become increasingly pervasive, finding applications across many industries and disciplines. Ensuring the trustworthiness of LLM outputs is paramount, where Uncertainty Estimation (UE) plays a key role. In this work, a comprehensive empirical study is conducted to examine the robustness and effectiveness of diverse UE measures regarding aleatoric and epistemic uncertainty in LLMs. It involves twelve different UE methods and four generation quality metrics including LLMScore from LLM criticizers to evaluate the uncertainty of LLM-generated answers in Question-Answering (QA) tasks on both in-distribution (ID) and out-of-distribution (OOD) datasets. Our analysis reveals that information-based methods, which leverage token and sequence probabilities, perform exceptionally well in ID settings due to their alignment with the model's understanding of the data. Conversely, density-based methods and the P(True) metric exhibit superior performance in OOD contexts, highlighting their effectiveness in capturing the model's epistemic uncertainty. Semantic consistency methods, which assess variability in generated answers, show reliable performance across different datasets and generation metrics. These methods generally perform well but may not be optimal for every situation.

• The paper presents a comprehensive evaluation of 12 uncertainty estimation methods applied to LLMs in both in-distribution and out-of-distribution QA settings.
• The methodology compares semantic, information-based, density-based, and reflexive techniques, highlighting that information-based methods excel on ID tasks while density-based and reflexive methods perform better on OOD tasks.
• The findings underscore the importance of selecting appropriate uncertainty metrics to improve trustworthiness and performance of LLM-driven QA systems in diverse contexts.

Measuring Aleatoric and Epistemic Uncertainty in LLMs: An Empirical Study

This essay provides an in-depth exploration and analysis of the paper titled "Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks" (2511.03166). This paper undertakes a comprehensive empirical investigation into the robustness and efficacy of different Uncertainty Estimation (UE) methods applied to LLMs across both In-Distribution (ID) and Out-of-Distribution (OOD) datasets in Question-Answering (QA) tasks. The nuanced approach considers aleatoric and epistemic uncertainties, with implications for developing trustworthiness in LLM outputs.

Introduction

LLMs are increasingly pervasive in various applications, necessitating a mechanism to ascertain the trustworthiness of their responses. Uncertainty estimation plays a critical role in this context, where high-quality, robust measures are needed to discern the model's confidence in generating responses. This paper evaluates twelve diverse UE methods in tandem with four generation quality metrics, including LLMScore from LLM criticizers, to assess uncertainty in LLM-generated answers. The empirical paper spans ID and OOD datasets, highlighting the challenges in evaluating random aleatoric uncertainty inherent within distribution data and epistemic uncertainty encountered with novel, unknown data.

Figure 1: Ranking of UE Methods on CoQA, using LLMScore with Gemma.

Uncertainty Estimation Methods

The paper categorizes UE methods into four primary classes based on their approach and underlying principles:

• Semantic Consistency Methods: These methods evaluate the variability in generated answers to assess uncertainty. Techniques like Semantic Entropy, Eigenvalue Laplacian, Eccentricity, Lexical Similarity, and Degree Matrix are employed to gauge similarities and consistency among generated answers.
• Information-Based Methods: Focused on token probabilities, methods such as Perplexity, Mean Token Entropy, Maximum Sequence Probability, and Monte Carlo Sequence Entropy assess uncertainty based on the model’s understanding of generated sequences.
• Density-Based Methods: These approaches, including Mahalanobis Distance Sequence Decoder and Robust Density Estimation, determine uncertainty by evaluating the density of responses, which is useful in OOD contexts.
• Reflexive Method P(True): This method queries the generating model to evaluate the correctness of its answers, offering insights into self-awareness of uncertainty.

  Figure 2: Ranking of UE Methods on bAbIQA, using LLMScore with Gemma.

Experiments and Results

The paper rigorously tests these UE methods across three datasets: CoQA, bAbIQA, and ALCUNA. CoQA and bAbIQA serve as ID datasets, whereas ALCUNA provides insights into OOD scenarios. Results indicate that information-based methods excel in ID contexts, offering robust performance related to aleatoric uncertainty. Conversely, density-based methods and P(True) demonstrate superior capability in OOD contexts, effectively managing epistemic uncertainty.

Key findings suggest semantic consistency methods are generally reliable but not universally optimal. Information-based methods align well with the structured nature of ID tasks due to their token probability reliance. Density-based methods reveal their adaptability to novel data in OOD contexts. Reflexive methods like P(True) shed light on the LLM's potential self-awareness in estimating its own uncertainty.

Figure 3: Ranking of UE Methods on ALCUNA, using LLMScore with Gemma.

Implications and Future Directions

The implications of this research span theoretical and practical domains. Practically, deploying the most suitable UE method based on data context and uncertainty type can enhance trust and efficacy in real-world applications of LLMs, such as QA systems and conversational agents. Theoretically, the paper advances understanding of the mechanisms underlying uncertainty, aiding in the architecture design and refinement of upcoming models.

Looking forward, the research encourages exploration of more OOD datasets and suggests pre-training models on specific tasks to evaluate effects on UE performance. Future investigation may also consider evolving model architectures, like newer iterations of LLMs, to assess their inherent uncertainty handling mechanisms.

Conclusion

This paper presents a thorough evaluation of UE methods applied to LLMs, revealing nuanced insights into aleatoric and epistemic uncertainties. The careful selection and application of these methods are crucial for optimizing LLM performance and ensuring trustworthy AI systems. By disentangling different uncertainty types and evaluating method effectiveness across diverse datasets, this work lays a robust foundation for future inquiries into improving the reliability of LLM outputs in complex, dynamic environments.