Context Selection

Context selection focuses on optimizing the information used by machine learning models, particularly large language models (LLMs), to improve accuracy and reduce errors like hallucinations. Current research emphasizes developing methods to automatically identify and prioritize relevant information within larger contexts, employing techniques like attention mechanisms and truth detection to filter out misleading or irrelevant data. This is crucial for enhancing the performance of LLMs in various applications, including information extraction, image captioning, and medical image analysis, leading to more reliable and efficient systems.