  Generative Large Language Models (LLMs) are widely utilized for their
excellence in various tasks. However, their tendency to produce inaccurate or
misleading outputs poses a potential risk, particularly in high-stakes
environments. Therefore, estimating the correctness of generative LLM outputs
is an important task for enhanced reliability. Uncertainty Estimation (UE) in
generative LLMs is an evolving domain, where SOTA probability-based methods
commonly employ length-normalized scoring. 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 considers the
semantic contribution of each token in the generated sequence in the context of
the question. We demonstrate that integrating 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. Code can be found https://github.com/Ybakman/LLM_Uncertainity.
