Multi Step Reasoning Benchmark

Multi-step reasoning benchmarks evaluate the ability of large language models (LLMs) to solve complex problems requiring multiple logical steps, a crucial area for advancing AI capabilities. Current research focuses on improving LLMs' performance on these benchmarks through techniques like code-based planning, symbolic backward chaining, and refined prompting methods that leverage native-speaking demonstrations to enhance chain-of-thought reasoning. These advancements aim to create more robust and efficient LLMs capable of handling diverse reasoning tasks, ultimately impacting fields like question answering, decision-making, and scientific discovery.