MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs

Overview of MARS

Abstract

Generative Large Language Models (LLMs) are widely utilized for their excellence in vari- ous tasks. However, their tendency to produce inaccurate or misleading outputs poses a po- tential risk, particularly in high-stakes environ- ments. Therefore, estimating the correctness of generative LLM outputs is an important task for enhanced reliability. Uncertainty Estima- tion (UE) in generative LLMs is an evolving domain, where SOTA probability-based meth- ods commonly employ length-normalized scor- ing. In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods. MARS is a novel scoring function that con- siders the semantic contribution of each to- ken in the generated sequence in the context of the question. We demonstrate that inte- grating MARS into UE methods results in a universal and significant improvement in UE performance. We conduct experiments using three distinct closed-book question-answering datasets across five popular pre-trained LLMs. Lastly, we validate the efficacy of MARS on a Medical QA dataset.

Publication
Annual Meeting of the Association for Computational Linguistics 2024
Yavuz Faruk Bakman
Yavuz Faruk Bakman
PhD Student in Computer Science

My research interests include Trustworthy LLM, Continual Learning and Federated Learning.