Overcoming "Lost-in-the-Middle": Examining GM-Extract and Mitigations for Large Language Models

This ArXiv study investigates methods to address the "Lost-in-the-Middle" problem, a crucial challenge for effective information retrieval in Large Language Models. The research likely offers valuable insights into improving LLM performance on tasks requiring contextual understanding.

• •Addresses the "Lost-in-the-Middle" problem, a key limitation in LLMs.
• •Investigates GM-Extract as a potential solution.
• •Explores mitigation strategies to improve contextual understanding.