KSOD: Knowledge Supplement for LLMs On Demand
This addresses LLMs' knowledge deficiencies for domain-specific applications, though it appears incremental as it builds on existing fine-tuning methods.
The paper tackles LLMs' errors in domain-specific tasks by proposing KSOD, a framework that identifies missing knowledge, tunes a knowledge module, and supplements LLMs with verified knowledge, enhancing performance on tasks requiring that knowledge while preserving performance on other tasks.
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet still produce errors in domain-specific tasks. To further improve their performance, we propose KSOD (Knowledge Supplement for LLMs On Demand), a novel framework that empowers LLMs to improve their capabilities with knowledge-based supervised fine-tuning (SFT). KSOD analyzes the causes of errors from the perspective of knowledge deficiency by identifying potential missing knowledge in LLM that may lead to the errors. Subsequently, KSOD tunes a knowledge module on knowledge dataset and verifies whether the LLM lacks the identified knowledge based on it. If the knowledge is verified, KSOD supplements the LLM with the identified knowledge using the knowledge module. Tuning LLMs on specific knowledge instead of specific task decouples task and knowledge and our experiments on two domain-specific benchmarks and four general benchmarks empirically demonstrate that KSOD enhances the performance of LLMs on tasks requiring the supplemented knowledge while preserving their performance on other tasks. Our findings shed light on the potential of improving the capabilities of LLMs with knowledge-based SFT.