CriticGPT is an approach to improving language models and task-oriented dialogue systems using offline reinforcement learning techniques.
It aims to learn an end-to-end task-oriented dialogue agent without diverging from natural human language.
The method starts by fine-tuning a pre-trained language model like GPT-2 and learning a critic model using a dialogue corpus.
CriticGPT then updates the policy through behavior cloning of critic-guided self-generated responses. This approach helps maintain natural language quality while improving task performance.
The system generates strategically promising actions based on the value estimated by the critic component.
In experiments, CriticGPT has been shown to outperform state-of-the-art algorithms on task-oriented dialogue benchmarks like MultiWOZ 2.0 and ConvLab.
The method can be adapted for use with various generative pre-trained language models, not just GPT-2.
CriticGPT represents an innovative approach to combining the strengths of large language models with reinforcement learning techniques to create more effective task-oriented dialogue systems. By using a critic to guide the generation process, it aims to maintain the fluency and naturalness of language model outputs while improving task-specific performance.