Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization
This addresses the subpar reasoning performance of LLMs, offering an automated alternative to human-crafted prompts, though it appears incremental as an early exploration.
The authors tackled the problem of improving reasoning in Large Language Models by introducing Combinatorial Reasoning (CR), a fully-automated prompting method that samples reasons and uses combinatorial optimization to select subsets for Chain-of-Thought prompts, with preliminary results suggesting it is a promising approach.
Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods.