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InfoQ Homepage News OpenAI's CriticGPT Catches Errors in Code Generated by ChatGPT

OpenAI's CriticGPT Catches Errors in Code Generated by ChatGPT

OpenAI recently published a paper about CriticGPT, a version of GPT-4 fine-tuned to critique code generated by ChatGPT. When compared with human evaluators, CriticGPT catches more bugs and produces better critiques. OpenAI plans to use CriticGPT to improve future versions of their models.

When originally developing ChatGPT, OpenAI used human "AI trainers" to rate the outputs of the model, creating a dataset that was used to fine-tune it using reinforcement learning from human feedback (RLHF). However, as AI models improve, and can now perform some tasks at the same level as human experts, it can be difficult for human judges to evaluate their output. CriticGPT is part of OpenAI's effort on scalable oversight, which is intended to help solve this problem. OpenAI decided first to focus on helping ChatGPT improve its code-generating abilities. The researchers used CriticGPT to generate critiques of code; they also paid qualified human coders to do the same. In evaluations, AI trainers preferred CriticGPT's critiques 80% of the time, showing that CriticGPT could be a good source for RLHF training data. According to OpenAI:

The need for scalable oversight, broadly construed as methods that can help humans to correctly evaluate model output, is stronger than ever. Whether or not RLHF maintains its dominant status as the primary means by which LLMs are post-trained into useful assistants, we will still need to answer the question of whether particular model outputs are trustworthy. Here we take a very direct approach: training models that help humans to evaluate models....It is...essential to find scalable methods that ensure that we reward the right behaviors in our AI systems even as they become much smarter than us. We find LLM critics to be a promising start.

Interestly, CriticGPT is also a version of GPT-4 that is fine-tuned with RLHF. In this case, the RLHF training data consisted of buggy code as the input, and a human-generated critique or explanation of the bug as the desired output. The buggy code was produced by having ChatGPT write code, then having a human contractor insert a bug and write the critique.

To evaluate CriticGPT, OpenAI used human judges to rank several critiques side-by-side; judges were shown outputs from CriticGPT and from baseline ChatGPT, as well as critiques generated by humans alone or by humans with CriticGPT assistance ("Human+CriticGPT"). The judges preferred CriticGPT's output over that of ChatGPT and human critics. OpenAI also found that the Human+CriticGPT teams' output was "substantially more comprehensive" than that of humans alone. However, it tended to have more "nitpicks."

In a discussion about the work on Hacker News, one user wrote:

For those new to the field of AGI safety: this is an implementation of Paul Christiano's alignment procedure proposal called Iterated Amplification from 6 years ago...It's wonderful to see his idea coming to fruition! I'm honestly a bit skeptical of the idea myself (it's like proposing to stabilize the stack of "turtles all the way down" by adding more turtles)...but every innovative idea is worth a try, in a field as time-critical and urgent as AGI safety.

Christiano formerly ran OpenAI's language model alignment team. Other companies besides OpenAI are also working on scalable oversight. In particular, Anthropic has published research papers on the problem, such as their work on using a debate between LLMs to improve model truthfulness.

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