KnowTuning: Knowledge-aware Fine-tuning for Large Language Models

ACL ARR 2024 April Submission409 Authors

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.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: Knowledge-intensive question answering, open-ended question answering, open-domain QA

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 409