Multi-step Large Language Models

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Source arXiv

Artificial Intelligence

Computation and Language

Multi-step Large Language Models

Faithful Reasoning Using Large Language Models

We show how large language models can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem.

Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned large language models, one for selection and one for inference, to produce a valid reasoning trace.

We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy, and generates humanly interpretable reasoning traces whose validity can be checked by the user.

Authors

Antonia Creswell, Murray Shanahan

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