K now T uning: Knowledge-aware Fine-tuning for Large Language Models (2024)

Abstract: Despite their success at many natural language processing (NLP) tasks, large language models still struggle to effectively leverage knowledge for knowledge-intensive tasks, manifesting limitations such as generating incomplete, non-factual, or illogical answers. These limitations stem from inadequate knowledge awareness of LLMs during vanilla fine-tuning. To address these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs. We devise a fine-grained knowledge augmentation stage to train LLMs to identify difficult fine-grained knowledge in answers. We also propose a coarse-grained knowledge comparison stage to train LLMs to distinguish between reliable and unreliable knowledge, in three aspects: completeness, factuality, and logicality. Extensive experiments on both generic and medical question answering (QA) datasets confirm the effectiveness of KnowTuning, through automatic and human evaluations, across various sizes of LLMs. We further verify that KnowTuning generates more facts with less factual error rate under fine-grained facts evaluation.

## introduction large language models (llms) have become a default solution for many natural language processing (nlp) scenarios, including the question answering (qa) task @xcite @xcite . to achieve strong performance, most llm first accumulate substantial knowledge by pre-training on extensive datasets @xcite . then, in the supervised fine-tuning (sft) stage, these llms further learn downstream domain knowledge and how to exploit the corresponding knowledge to answer diverse questions @xcite @xcite ## method in this section, we detail the knowtuning method. first, we introduce the preliminaries. then, we introduce the fine-grained knowledge augmentation. next, we introduce coarse-grained knowledge comparison in detail. finally, a training process for knowtuning is explained. ## experimental results and analysis to answer our research questions, we conduct generic domain and medical domain qa experiments, fine-grained facts evaluation, and ablation studies. in addition, we conducted a case study to gain further understanding of the effectiveness of knowtuning. ## conclusions in this paper, we focus on improving the knowledge awareness of llms via fine-tuning for complex knowledge-intensive tasks. we have proposed knowtuning to fine-tune llms through fine-grained knowledge augmentation and coarsegrained knowledge comparison stages. we have conducted comprehensive experiments on generic and medical domain qa datasets, demonstrating the effectiveness of knowtuning through automatic and human evaluations, across various sizes of llms. moreover, knowtuning generates more facts with less factual error rate under fine-grained facts evaluation.