Large Language Models Exhibit Bias Towards Forward Planning: A Flipped-Problem Approach to Enhance Backward Planning

While Large Language Models (LLMs) show promise in planning tasks, they exhibit a significant bias towards forward planning. This paper introduces a novel approach of "flipping" the problem, allowing LLMs to plan forward on the inverted task, thereby mitigating the backward bias and improving overall planning success.