Answer Candidate

Answer candidate selection focuses on improving the quality of responses generated by large language models (LLMs) by evaluating and ranking multiple potential answers. Current research emphasizes refining ranking methods through techniques like repeated evaluations to ensure consistency and incorporating contextual information, such as dialogue history or retrieved evidence, to improve accuracy. This work is crucial for enhancing the reliability and performance of LLMs in various applications, including question answering, dialogue systems, and medical AI, by addressing issues like inconsistent rankings and conflicting information sources.