DeepMind's Selection-Inference Language Model System Generates Humanly Interpretable Reasoning Traces

Explainability is one of the most pressing concerns in machine learning research and development. Although contemporary large-scale language models (LMs) have demonstrated impressive question-answering capabilities, their inherent opacity can conceal just how these models reach their final answers, making it difficult for users to spot any possible mistakes or justify the outputs. A DeepMind research team addresses this issue in the new paper Faithful Reasoning Using Large Language Models, proposing a forward-chaining selection-inference model that can perform faithful reasoning and provide a valid reasoning trace to improve reasoning quality and help users check and validate the final answers. The proposed approach is based on the idea that LMs can perform faithful multi-step reasoning if the underlying logical structure of a given problem can be mirrored by a causal structure. To realize this, the team developed selection-inference (SI) as their system's backbone, a novel architecture comprising two fine-tuned language models: one for selection and one for inference.