Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator

• URL: http://arxiv.org/abs/2412.09280v1
• Date: Thu, 12 Dec 2024 13:42:58 GMT
• Title: Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator
• Authors: Chengyuan Liu, Shihang Wang, Lizhi Qing, Jun Lin, Ji Zhang, Fei Wu, Kun Kuang,
• Abstract summary: We propose a pipeline to solve the domain-specific calculation problems with Knowledge-Intensive Programs Generator. It generates knowledge-intensive programs according to the domain-specific documents. We also find that the code generator is also adaptable to other domains, without training on the new knowledge.
• Score: 33.680619900836376
• License:
• Abstract: Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some domain-specific tasks. In this paper, we investigate into knowledge-intensive calculation problems. We find that the math problems to be challenging for LLMs, when involving complex domain-specific rules and knowledge documents, rather than simple formulations of terminologies. Therefore, we propose a pipeline to solve the domain-specific calculation problems with Knowledge-Intensive Programs Generator more effectively, named as KIPG. It generates knowledge-intensive programs according to the domain-specific documents. For each query, key variables are extracted, then outcomes which are dependent on domain knowledge are calculated with the programs. By iterative preference alignment, the code generator learns to improve the logic consistency with the domain knowledge. Taking legal domain as an example, we have conducted experiments to prove the effectiveness of our pipeline, and extensive analysis on the modules. We also find that the code generator is also adaptable to other domains, without training on the new knowledge.

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