Large Language Models Can Self-Improve in Long-context Reasoning

This research paper investigates the potential for large language models (LLMs) to self-improve in long-context reasoning, which involves processing and understanding complex information spread across long stretches of text. The authors propose a novel approach called SEALONG that leverages the LLMs' ability to generate multiple outputs for a given question and then scores these outputs using a method called Minimum Bayes Risk (MBR). The MBR approach prioritizes outputs that align better with each other, thereby filtering out outputs that might be incorrect or hallucinatory. SEALONG then…