Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models

Problem
Large language models (LLMs) exhibit high performance across various tasks but often produce outputs that are factually incorrect, lacking explicit uncertainty estimates. This gap complicates users’ ability to assess the reliability of model responses. Existing methods for uncertainty quantification primarily rely on indirect signals, such as entropy from sampled outputs, which can be challenging to interpret and do not fully utilize the model’s inherent capability to evaluate its own uncertainty. This paper addresses this gap by proposing a straightforward self-assessment method for uncertainty quantification in LLMs. The work is presented as a preprint and has not undergone peer review.

Method
The proposed method involves clustering sampled generations into semantically distinct groups. Each cluster is transformed into structured multiple-choice questions, where the LLM assigns probabilities to each answer option. The confidence estimate for the model’s output is derived from these probabilities. The authors utilize various LLM architectures and datasets, although specific details regarding the architectures and training compute are not disclosed. The method is designed to be efficient, requiring only two additional samples to achieve competitive performance compared to existing uncertainty quantification techniques.

Results
The proposed self-assessment method demonstrates superior performance across multiple models and datasets when compared to baseline approaches. The authors report that their method consistently outperforms existing techniques, achieving competitive results with minimal additional sampling. Specific performance metrics are not detailed in the abstract, but the emphasis on efficiency suggests a significant reduction in the number of samples needed for reliable uncertainty quantification.

Limitations
The authors acknowledge that their method’s effectiveness may vary depending on the specific characteristics of the LLMs and datasets used. They do not address potential limitations related to the scalability of the clustering approach or the interpretability of the generated multiple-choice questions. Additionally, the reliance on clustering may introduce biases based on the clustering algorithm’s sensitivity to the input data distribution.

Why it matters
This work has significant implications for the deployment of LLMs in real-world applications, where understanding the reliability of model outputs is crucial. By providing a more interpretable and efficient method for uncertainty quantification, this approach can enhance user trust and facilitate better decision-making based on model outputs. The findings contribute to the ongoing discourse on improving LLM reliability and interpretability, as discussed in related literature on uncertainty quantification in AI systems, and are available on arXiv.