Answer Distribution

Answer distribution analysis in various AI models, particularly large language models (LLMs), focuses on understanding how model responses vary across different inputs and contexts, aiming to identify and mitigate biases. Current research investigates the impact of factors like surface form in questions, prior knowledge integration, and dataset biases on answer distributions, employing techniques such as self-consistency and mutual information analysis. These studies are crucial for improving model robustness and reliability, particularly in applications like question answering systems and remote sensing, where biased outputs can lead to inaccurate or unfair results. Ultimately, a deeper understanding of answer distribution is essential for building more trustworthy and equitable AI systems.