The longer a conversation goes, the more likely that a large language model (LLM) will go astray. A research paper from Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, and Jennifer Neville finds that most models lose aptitude—and unreliability skyrockets—in multi-turn exchanges: We find that LLMs often make assumptions in early turns and prematurely attempt to generate final solutions, on which they overly rely. In simpler terms, we discover that when LLMs take a wrong turn in a conversatio...| Big Medium - Full Feed