AI Seems to Do Better on Tasks When Asked to Reflect on Its Mistakes

In a not-yet-peer-reviewed paper, a team of researchers from Northeastern University and the Massachusetts Institute of Technology suggests that large language models (LLM) might be able to learn from their own mistakes — just like humans. Teaching them to do so, they say, might be able to push AI technologies into a new phase of autonomous problem-solving. “Self-reflection allows humans to efficiently solve novel problems through a process of trial and error,” the researchers write in the paper. “Building on recent research, we propose Reflexion, an approach that endows an agent with dynamic memory and self-reflection capabilities to enhance its existing reasoning trace and task-specific action choice abilities.” In other words, their methodology dubbed “Reflexion” is a framework for teaching AI models via prompts to apply a trial-and-error technique to their outputs. So, just like us, if at first, they don’t succeed, they can try, try again. Testing their new framework was a relatively simple process. The machine, or “agent,” was presented with problem-solving tasks and asked to complete them; when it messed up, it was prompted with the Reflexion technique to find those mistakes for itself — a process that they claim helps the program evolve, just like humans. “To achieve full automation, we introduce a straightforward yet effective heuristic that enables the agent to pinpoint hallucination instances, avoid repetition in action sequences, and, in some environments, construct an internal memory map of the given environment,” the researchers write in their paper. Using a series of standardized “decision-making tasks,” the researchers…AI Seems to Do Better on Tasks When Asked to Reflect on Its Mistakes

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