Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling (2311.08718v2)

Abstract: Uncertainty decomposition refers to the task of decomposing the total uncertainty of a predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the data-generating process, and epistemic (model) uncertainty, resulting from missing information in the model's training data. In LLMs specifically, identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability, but remains an important open research question. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling, which can be applied to any pre-trained LLM. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions. We show that, when aleatoric uncertainty arises from ambiguity or under-specification in LLM inputs, this approach makes it possible to factor an (unclarified) LLM's predictions into separate aleatoric and epistemic terms, using a decomposition similar to the one employed by Bayesian neural networks. Empirical evaluations demonstrate that input clarification ensembling provides accurate and reliable uncertainty quantification on several language processing tasks. Code and data are available at https://github.com/UCSB-NLP-Chang/LLM_uncertainty.

Decomposing Uncertainty for LLMs through Input Clarification Ensembling

The paper, "Decomposing Uncertainty for LLMs through Input Clarification Ensembling," presents a novel framework aimed at addressing the challenge of uncertainty decomposition in LLMs. The paper posits that effective decomposition of uncertainty in LLMs into aleatoric and epistemic components is crucial for enhancing the reliability, trustworthiness, and interpretability of these models. However, existing methods, such as the Bayesian Neural Network (BNN), are unsuitable for LLMs due to their prohibitive size and the computational expense of training multiple model variants. To circumvent these challenges, the authors introduce an alternative approach: input clarification ensembling.

Key Contribution and Methodology

The contribution of this work is the proposal of an input clarification ensemble framework, which allows for uncertainty decomposition without the need to train new models. Instead of varying model parameters, this method generates multiple clarifications for a given input, introduces these clarifications into the existing LLM, and ensembles the resultant predictions. This approach mirrors the structure of BNNs but operates at the level of input rather than model variability.

The framework quantifies two principal types of uncertainty in LLMs:

1. Aleatoric uncertainty (data uncertainty): This is quantified as the inherent complexity or ambiguity in the input data, which remains irresolvable despite further learning. For instance, the question "Who is the president of this country?" is inherently ambiguous without additional context.
2. Epistemic uncertainty (model uncertainty): This results from limited model knowledge and can be reduced with access to additional data or model refinement.

Empirical evaluations demonstrate that this framework provides accurate uncertainty quantification across various tasks such as ambiguity detection and mistake detection, leveraging public datasets like Natural Questions and synthetic datasets.

Implications and Future Directions

This research has significant implications for the field of AI and LLMs. By providing a clear method for uncertainty decomposition, it opens pathways for building more interpretable and trustworthy LLMs. Such decomposition allows users to discern whether uncertainty in model predictions stems from data ambiguity or model limitations, guiding appropriate reactions—whether refining model knowledge or improving data clarity.

The proposed framework also suggests future avenues for research, such as developing more sophisticated clarification models to enhance input processing. Furthermore, there is potential for integrating such decomposition techniques with real-world applications, enhancing the robustness and reliability of AI systems in sensitive domains, such as healthcare or autonomous systems.

In future developments, expanding the scope of this framework to encompass region-specific clarifications or domain-specific knowledge bases could yield further improvements in uncertainty quantification. Additionally, the flexibility and scalability of this framework could position it for adaptation to emerging AI architectures and paradigms, reinforcing its utility in continually evolving AI landscapes.