Contrastive Learning for Knowledge-Based Question Generation in Large Language Models
This addresses the need for high-quality question generation in question-and-answer systems, though it appears incremental as it builds on existing contrastive learning and prompting techniques.
The paper tackled the problem of hallucination and knowledge gaps in large language models for knowledge-based question generation by proposing a method that incorporates contrastive learning with prompts containing contrasting examples, resulting in considerable performance improvements, particularly with simultaneous use of contrasting instructions and examples for highest quality and improved accuracy.
With the rapid development of artificial intelligence technology, especially the increasingly widespread application of question-and-answer systems, high-quality question generation has become a key component in supporting the development of these systems. This article focuses on knowledge-based question generation technology, which aims to enable computers to simulate the human questioning process based on understanding specific texts or knowledge bases. In light of the issues of hallucination and knowledge gaps present in large-scale language models when applied to knowledge-intensive tasks, this paper proposes an enhanced question generation method that incorporates contrastive learning. This method utilizes multiple models to jointly mine domain knowledge and uses contrastive learning to guide the model in reducing noise and hallucinations in generation. Experimental results show that by designing prompts containing contrasting examples, the model's performance in question generation improves considerably, particularly when contrasting instructions and examples are used simultaneously, leading to the highest quality of generated questions and improved accuracy. These results demonstrate that the method proposed in this study, which combines contrasting context and chain-of-thought prompts, can effectively improve both the quality and the practicality of question generation.