DeepMind’s GenRM improves LLM accuracy by having models verify their own outputs
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Large language models (LLMs) are prone to factual and logical errors, especially when dealing with complex reasoning tasks. To address this challenge, researchers often use verifiers or reward models to evaluate and select the most accurate responses from a set of LLM-generated outputs.
In a new paper, researchers at Google DeepMind, University of Toronto, Mila, and UCLA introduce GenRM, a novel approach that leverages the generative capabilities of LLMs to create more effective verifiers. GenRM can be a practical tool for LLM applications where current verification methods fail.
One of the common methods to improve the accuracy of LLMs is to have them generate several candidate answers and then use a separate component to select the best one. This approach requires a reliable verifier or reward model.
In reasoning domains, LLM-based verifiers are typically trained as discriminative reward models (RMs) to assign numerical scores to candidate solutions, which are then used to classify them as correct or incorrect. However, these RMs do not fully use the strengths of LLMs in generating and processing responses.
