KnowMap: Efficient Knowledge-Driven Task Adaptation for LLMs

Kelin Fu; Kaigui Bian

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arXiv · 2506.19527

KnowMap: Efficient Knowledge-Driven Task Adaptation for LLMs

Kelin Fu·Kaigui Bian

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Abstract

While Large Language Models (LLMs) possess significant capabilities in open-world agent tasks, they also face challenges in rapidly adapting to new, specialized tasks due to their reliance on static pre-trained knowledge. Traditional methods such as fine-tuning are often costly, data-intensive, and may lead to "catastrophic forgetting." Therefore, we present KnowMap, a novel approach that dynamically constructs a knowledge base from environmental and experiential data. KnowMap fine-tunes a small knowledge-embedding model to equip a larger LLM with valuable task-specific knowledge. Our experiments on the ScienceWorld benchmark demonstrate 17.71% improvement for the performance of gpt-4-turbo model. KnowMap not only provides an efficient and effective means for LLM task-adapting, but also highlights how integrating environmental and experiential knowledge can enhance LLMs' reasoning capabilities.

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Kelin Fu, Kaigui Bian. 2025-06-24. KnowMap: Efficient Knowledge-Driven Task Adaptation for LLMs. https://arxiv.org/abs/2506.19527

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