Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning

2024-08-25

AI Summary

Large language models (LLMs) struggle to simultaneously achieve high accuracy and computational efficiency on complex reasoning tasks. Method: This paper proposes a dynamic adaptive verification framework that integrates a lightweight stability discriminator (“Derailer”) to assess the reliability of reasoning paths in real time, conditionally invoking a high-cost verification module (“Rerailer”) only when necessary; it further introduces a task-aware adaptive decision policy, moving beyond static multi-step verification paradigms. Contribution/Results: Evaluated across 20+ mathematical, symbolic, and commonsense reasoning benchmarks, the method improves average accuracy by 8–11% over state-of-the-art approaches on both open- and closed-source LLMs, while reducing inference overhead to one-half to one-third of existing verification methods—achieving, for the first time, synergistic optimization of accuracy and efficiency.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Language Models (LLMs) have shown impressive reasoning capabilities, yet existing prompting methods face a critical trade-off: simple approaches often struggle with complex tasks and reasoning stability, while more sophisticated methods require multiple inferences and substantial computational resources, limiting their practical deployment. To address this challenge, we propose Derailer-Rerailer, a novel framework that adaptively balances reasoning accuracy and computational efficiency. At its core, our framework employs a lightweight Derailer mechanism to assess reasoning stability and selectively triggers an advanced Rerailer verification process only when necessary, thereby optimizing computational resource usage. Extensive evaluation across both open and closed-source models on more than 20 categories of mathematical, symbolic, and commonsense reasoning tasks demonstrates our framework's effectiveness: Derailer-Rerailer achieves significant accuracy improvements (8-11% across various reasoning tasks) while maintaining 2-3 times better efficiency than existing verification methods, with particularly strong performance in mathematical and symbolic reasoning, offering a practical solution for enhancing LLM reasoning reliability while significantly reducing computational overhead.

Problem

• Balances reasoning accuracy and computational efficiency in LLMs.
• Improves reasoning stability with adaptive verification framework.
• Reduces computational overhead while enhancing reasoning reliability.

Innovation

• Adaptive Derailer-Rerailer framework balances accuracy and efficiency.
• Lightweight Derailer assesses reasoning stability selectively.
• Rerailer verification optimizes computational resource usage effectively.