VERUS-LM：一种结合大语言模型与符号推理的多功能框架
VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning
AI总结：
VERUS-LM通过结合大语言模型与符号推理，提升复杂推理任务的适应性与效率，实现更广泛的推理能力。
摘要：
最近一种神经符号推理的方法是明确结合大语言模型（LLMs）和符号求解器的优势来解决复杂推理任务。然而，当前方法面临显著限制，包括由于任务特定提示导致的泛化能力差，由于知识和查询之间缺乏分离导致的效率低下，以及推理能力受限。这些不足阻碍了其在不同领域中的可扩展性和适用性。在本文中，我们引入了VERUS-LM，一种新的框架，旨在解决这些挑战。VERUS-LM采用通用提示机制，明确将领域知识与查询分开，并支持多种不同的逻辑推理任务。该框架增强了适应性，降低了计算成本，并允许更丰富的推理形式，如优化和约束满足。我们证明了我们的方法在新的数据集上实现了多样化的推理，并明显优于LLMs。此外，我们的系统在常见的推理基准测试中与类似最先进的方法相比取得了具有竞争力的结果，并在困难的AR-LSAT数据集上显著超越了它们。通过推动混合推理的边界，VERUS-LM代表了迈向更多功能的神经符号AI系统的重要一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
