将大模型与符号推理结合，提升复杂任务的通用性和效率。
VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning
- 用通用提示机制分离知识与查询，支持多种逻辑推理任务。
- 在新数据集上表现显著优于纯大模型，在AR-LSAT上大幅领先。
- 适合需要强推理能力的复杂场景，如优化与约束求解。
近期神经符号推理方法尝试结合大语言模型（LLMs）与符号求解器的优势以应对复杂推理任务。然而，现有方法存在任务特定提示导致泛化性差、知识与查询未分离造成效率低下、推理能力受限等问题，制约其在多领域扩展。本文提出VERUS-LM框架，采用通用提示机制，清晰分离领域知识与查询，并支持多样逻辑推理任务。该框架提升了适应性，降低计算开销，支持优化与约束满足等丰富推理形式。实验表明，该方法在新构建的数据集上显著优于纯大模型；在常见推理基准上达到顶尖水平，在困难的AR-LSAT数据集上实现显著超越。VERUS-LM推动了混合推理边界，是迈向更通用神经符号系统的重要进展。
原文摘要 · Abstract (English)
A recent approach to neurosymbolic reasoning is to explicitly combine the strengths of large language models (LLMs) and symbolic solvers to tackle complex reasoning tasks. However, current approaches face significant limitations, including poor generalizability due to task-specific prompts, inefficiencies caused by the lack of separation between knowledge and queries, and restricted inferential capabilities. These shortcomings hinder their scalability and applicability across diverse domains. In this paper, we introduce VERUS-LM, a novel framework designed to address these challenges. VERUS-LM employs a generic prompting mechanism, clearly separates domain knowledge from queries, and supports a wide range of different logical reasoning tasks. This framework enhances adaptability, reduces computational cost, and allows for richer forms of reasoning, such as optimization and constraint satisfaction. We show that our approach succeeds in diverse reasoning on a novel dataset, markedly outperforming LLMs. Additionally, our system achieves competitive results on common reasoning benchmarks when compared to similar state-of-the-art approaches, and significantly surpasses them on the difficult AR-LSAT dataset. By pushing the boundaries of hybrid reasoning, VERUS-LM represents a significant step towards more versatile neurosymbolic AI systems.
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