  Large Language Models (LLMs) frequently struggle with complex reasoning
tasks, failing to construct logically sound steps towards the solution. In
response to this behavior, users often try prompting the LLMs repeatedly in
hopes of reaching a better response. This paper studies such repetitive
behavior and its effect by defining a novel setting, Chain-of-Feedback (CoF).
The setting takes questions that require multi-step reasoning as an input. Upon
response, we repetitively prompt meaningless feedback (e.g. 'make another
attempt') requesting additional trials. Surprisingly, our preliminary results
show that repeated meaningless feedback gradually decreases the quality of the
responses, eventually leading to a larger deviation from the intended outcome.
To alleviate these troubles, we propose a novel method, Recursive
Chain-of-Feedback (R-CoF). Following the logic of recursion in computer
science, R-CoF recursively revises the initially incorrect response by breaking
down each incorrect reasoning step into smaller individual problems. Our
preliminary results show that majority of questions that LLMs fail to respond
correctly can be answered using R-CoF without any sample data outlining the
logical process.
