From Response to Query: The Power of Reverse Thinking in Language Models

Recent advancements in large language models (LLMs) have primarily focused on enhancing their capacity to predict text in a forward, time-linear manner. However, emerging research suggests that enabling LLMs to critique and refine their own outputs retrospectively can significantly improve their performance. While effective, existing methods rely on the advanced reasoning and instruction-following abilities inherent to high-capacity LLMs. Moreover, these approaches often involve sequential processing of generated responses, resulting in considerable increases in inference time.

In a new paper Time-Reversal Provides Unsupervised Feedback to LLMs, a research team from Google DeepMind and Indian Institute of Science proposes Time Reversed Language Models (TRLMs), a framework that allows LLMs to reason in reverse — scoring and generating content in a manner opposite to the traditional forward approach. Unlike conventional LLMs, which predict responses based on queries, TRLMs predict or evaluate queries based on responses, thereby facilitating unsupervised feedback during inference.