A GAIL Fine-Tuned LLM Enhanced Framework for Low-Resource Knowledge Graph Question Answering

Recent studies on knowledge graph question answering (KGQA) have focused on tackling complex inquiries to enhance the applicability of models in real-life settings. Unfortunately, KGQA models encounter significant challenges due to the lack of high-quality annotated data, making it difficult to accurately answer the diverse range of complex natural language questions posed by users. Inspired by the recent success of Large Language Models (LLMs), the burden associated with manual annotation can be mitigated by utilizing LLMs. However, the data generated directly by LLMs may exhibit a potential distribution discrepancy with real user queries. In this paper, we present an enhancement framework that utilizes Generative Adversarial Imitation Learning (GAIL) to fine-tune LLMs, which can address the challenges inherent in the low-resource KGQA task. Specifically, based on GAIL, the LLMs act as the generator aiming to output samples resembling expert demonstrations. Meanwhile, we utilize a paired discriminator to assess the authenticity of generated sequences and their relevance to the input SPARQL queries. Additionally, proximal policy optimization is leveraged to stabilize the training of the generator. Furthermore, we employ an automated algorithm to controllably sample various SPARQL queries from the knowledge graph, subsequently transforming them into corresponding natural language questions using fine-tuned LLMs. The synthetic dataset can serve as supplementary data for training lightweight KGQA models in real-world scenarios. Experimental results on the WebQuestionsSP, ComplexWebQuestions, and GrailQA show that our framework achieves state-of-the-art performance in a low-resource setting, even approaching the performance of supervised models.