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  • Predict-then-Verify Approaches in LLM Reasoning

Verifiers in LLM Reasoning

A core challenge in predict-then-verify approaches is the evaluation of an LLM's reasoning results or intermediate steps. To address this, 'verifiers' have been developed and implemented. These are specialized components designed to assess and validate the outputs generated during the LLM's reasoning process.

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1

3 months ago

Contributors are:

Gemini AI
Gemini AI
🏆 10

Who are from:

Google
Google
🏆 10

References

Tags

Ch.3 Prompting - Foundations of Large Language Models

Foundations of Large Language Models

Foundations of Large Language Models Course

Computing Sciences

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  • Verifier

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Learn After

  • Supervised Learning of Verifiers

  • Relation between Verifiers and RLHF Reward Models

  • Classification of Verification Approaches

  • Guiding Role of the Verifier in Self-Refinement

  • A system is designed to solve complex, multi-step logic puzzles. First, a generative model produces five different potential step-by-step solutions to a given puzzle. Then, a second, distinct component is used. This second component's sole function is to evaluate each of the five proposed solutions by scoring the logical soundness of each step in the reasoning chain. Based on these scores, it selects the single most coherent and valid solution to present as the final answer. What is the primary role of this second component in the system's architecture?

  • Improving an AI Tutoring System

  • Consider a system that solves a problem by first having one component generate several different step-by-step solutions. For this system to be effective, the same component that generated the solutions must also be used to evaluate them and select the best one.

  • You are reviewing a proposed architecture for an i...

  • You’re designing an internal LLM assistant for a f...

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