Researchers from Google DeepMind and University of Southern California have developed a new technique called SELF-DISCOVER that allows large language models (LLMs) like GPT-4 and PaLM to dynamically compose reasoning structures to solve complex problems.
The key innovation in SELF-DISCOVER is enabling LLMs to select relevant reasoning skills, adapt them to a specific task, and combine them into an executable structure – all without any training data or human involvement. For example, when faced with a mathematical word problem, the LLM may choose skills like “break into subproblems”, “propose and verify”, and “step-by-step reasoning”, then adapt them into a structured plan to decompose the problem, verify intermediate steps, and methodically reach the solution.
Through meta-learning prompts, the researchers guide the LLM to go through these three steps of selecting, adapting and implementing reasoning skills on a given task. This allows the model to uncover the intrinsic reasoning structure needed to efficiently solve that task.
Experiments across challenging benchmarks like BigBench-Hard, agent reasoning tests, and mathematical word problems show SELF-DISCOVER substantially boosts reasoning capabilities of GPT-4 and PaLM 2-L. On 25 complex reasoning tasks, it improved accuracy by 11% over chain-of-thought prompting and up to 29% over direct answering using GPT-4. The discovered reasoning structures also transferred well from GPT-4 to other models like Llama2-70B, demonstrating universality.
Compared to inference-heavy methods like self-consistency, SELF-DISCOVER achieved superior accuracy with 10-40x fewer inference calls. It also outperformed prompt optimization techniques that require training data. This makes the approach highly efficient.
The researchers suggest that by self-composing reasoning rather than relying on a fixed prompting style, LLMs can better adapt to diverse real-world problems. Just like programmers combine basic constructs, LLMs can learn to choose and integrate reasoning skills dynamically.
This work opens exciting avenues for structured reasoning with LLMs. The human-like reasoning composition in SELF-DISCOVER could enable collaborative problem-solving between humans and AI. With further research, it may be possible to build LLMs that learn richer reasoning strategies and unlock their full potential on complex cognitive tasks.