LLM Explanation

Research on Large Language Model (LLM) explanations focuses on understanding how LLMs generate explanations, evaluating their quality and faithfulness, and mitigating potential risks like bias and memorization. Current work explores various LLM architectures and prompting techniques to improve explanation generation across diverse domains, including medicine, quantum computing, and code. This research is crucial for enhancing trust in LLMs, improving their usability in high-stakes applications, and addressing ethical concerns related to transparency and accountability.