OpenAI Creates CriticGPT to Catch Errors From ChatGPT - IEEE Spectrum
CriticGPT is designed to help detect hallucinations as models become more advanced. One of the challenges with the sophisticated language models that drive chatbots like ChatGPT is the uncertainty in trusting their responses. While these models can provide accurate and useful information, they are also prone to generating false information, presenting it in a coherent manner, and relying on users to identify errors.
The Need for CriticGPT
OpenAI has taken a significant step towards addressing this issue by introducing a tool to assist human trainers in guiding language models towards truth and precision. The development of this tool is a part of the broader "alignment" work, where researchers aim to align the objectives of AI systems with human objectives. The focus of the new tool is on reinforcement learning from human feedback (RLHF), a critical technique in refining language models for public use.
Introducing CriticGPT
OpenAI researchers have trained a model called CriticGPT to evaluate the responses generated by ChatGPT. Initially, the tests involved ChatGPT producing computer code instead of text responses to facilitate error identification. The ultimate goal is to leverage CriticGPT to enhance the accuracy of language models through human feedback.
Results and Implications
The experiments conducted with CriticGPT yielded promising results. CriticGPT outperformed human code reviewers by identifying a significantly higher percentage of bugs. Combining CriticGPT with human trainers resulted in more comprehensive feedback and fewer false bug identifications compared to human evaluations alone. OpenAI is now working on integrating CriticGPT into its training pipelines to enhance the overall model training process.
Limitations and Future Directions
It is essential to acknowledge the limitations of the research, particularly its focus on short code snippets. While there are mentions of potential applications in catching errors in text responses, further exploration is required in this area. Additionally, the researchers recognize that CriticGPT may not be effective in addressing biases or contentious topics in AI responses.
Despite the inherent challenges, the introduction of CriticGPT marks a significant advancement in training more aligned and reliable AI models. By combining the strengths of human judgment with AI capabilities, researchers are paving the way for more effective model training and error detection in AI systems.
