Subjective Question

Subjective question answering focuses on evaluating and improving how large language models (LLMs) handle questions requiring opinion, interpretation, or personal experience, rather than factual recall. Current research emphasizes quantifying the biases and inconsistencies in LLM responses to subjective queries by comparing them to human survey data and developing new evaluation metrics that better capture semantic nuances. This work is crucial for understanding and mitigating the potential for LLMs to reflect and amplify societal biases, and for building more reliable and nuanced AI systems capable of handling the complexities of human communication and decision-making. The development of novel neuro-symbolic approaches and improved evaluation methods are key areas of ongoing investigation.