Choice Question Answering

Choice question answering (CQA) focuses on evaluating large language models (LLMs) by their ability to select the correct answer from a set of options, mirroring common assessment formats. Current research emphasizes improving LLM performance on CQA tasks by addressing limitations like format-specific biases, enhancing factual accuracy through context retrieval, and developing robust methods for detecting hallucinations. This research is crucial for advancing LLM reliability and trustworthiness, particularly in high-stakes domains like healthcare, where accurate and explainable answers are paramount.