Research on Controllable Output Methods of Large Models Fine-tuned Based on Reading Comprehension Ability

Large Language Models (LLMs) confront several challenges, including the generation of repetitive and incorrect outputs, as well as the occurrence of hallucinations. An effective strategy to mitigate these issues involves applying verifiable text generation, which prompts LLMs to produce content that can be accurately verified through citations. However, generating verifiable text presents significant challenges due to the complex relationships between long texts and their references. This study introduces an innovative reinforcement learning-based approach to address the problems of hallucinations and repetitive outputs in large models, particularly in the context of government question-answering services. The proposed method is exhaustively compared with existing algorithms such as Learning to Inject Fact Errors (LIFE) and Verifiable Text Generation (VTG). Extensive experiments conducted across three datasets demonstrate that our OutputControllable Reading Comprehension Fine-tuning Training Mechanism (OCRCFTM) significantly outperforms existing algorithms, improving reading comprehension abilities of large models by over $30 \%$.