OpenAI are starting to integrate such models into our #RLHFalignmentPipeline
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
Have you wondered how well ChatGPT can catch errors in its own output? OpenAI just shared the results of their own progress where a specialized fine-tuned model - CriticGPT - was trained to critique ChatGPT output. How I understood the paper (criticism invited): 1. Purpose of CriticGPT - AI model designed to critique and catch errors in AI-generated content - Initially focused on code, but demonstrated capability to evaluate general AI outputs - Aims to enhance safety and reliability of AI outputs across various domains 2. Performance Improvements - CriticGPT caught 85% of inserted bugs - Human contractors alone caught 25% of bugs - Represents a 3.4x improvement in bug detection 3. Human-AI Collaboration - Human+CriticGPT teams produce more comprehensive critiques - Reduces hallucination rates compared to AI-only critiques - CriticGPT critiques preferred 63% of the time over human-only critiques 5. Potential Impact on AI Development - Improves quality control in AI-generated content - Could help identify and mitigate risks in more advanced AI models 6. Limitations and Considerations - Focused primarily on code; may need adaptation for other domains - Still requires human oversight and refinement - Effectiveness on more advanced AI models remains to be seen Not to be overlooked, CriticGPT surfaced issues in the ChatGPT training set previously labeled as "flawless"... of those issues, Human reviewers taking another look agreed (24% of the time) that the data rating should be substantially decreased. Keep in mind - "The ultimate goal of scalable oversight is to help humans evaluate model output in order to train better and safer policies." https://lnkd.in/g9VFtjQK
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
We can generate code with OpenAI GPT-4 and make our work easier, but what if the code contains errors? OpenAI introduced its new artificial intelligence model CriticGPT. It was stated that the new model will help users detect possible errors in the codes produced by ChatGPT. #OpenAI #CriticGPT
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
Finding GPT-4’s mistakes with GPT-4! What an idea OpenAI. Read the full article to know more! #openai #chatgpt4 #algorithmx
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
Industrial scientist and developer focusing on robust AI systems and evaluation frameworks.
Who judges the judges? That fundamental question about safety with noisy experts needs to be answered. How would we know the supervisors, whether humans or robots, are themselves okay? To tackle this difficult engineering and philosophical problem one needs to understand the logic of unsupervised evaluation. Get a glimpse at how it works by checking out my Python "ntqr" package - https://lnkd.in/gypDcd7H
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
C++ Developer | Cybersecurity Researcher | Software Engineer | Backend Developer | Ph.D. Computer Scientist.
I see this related to LLM02-Insecure Output Handling and also to Developing_Insecure_Source_Code, one of the new candidate entries submitted for consideration to be included in version 2.0 of the OWASP Top 10 For Large Language Model Applications list (https://lnkd.in/gBPZZws7). The use of CriticGPT highlights the necessity of actively monitoring and sanitizing the model's inputs and outputs. #llmsecurity #aisecurity
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
Critic GPT can be designed to write automated tests. By leveraging its understanding of natural language and code, it can generate relevant test cases and scripts. Some might think this will take our jobs, but I find it pretty awesome.
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
Research Software Engineer @ IBM | Ex-Amazon Intern | Passionate About Data Science and its Real-World Applications
In a fascinating development, OpenAI has unveiled CriticGPT - an innovative AI model designed to catch errors in ChatGPT's code output. CriticGPT, based on the powerful GPT-4 model, has been specifically trained to spot mistakes in ChatGPT's responses. The results are impressive: when people use CriticGPT to review ChatGPT code, they outperform those without AI assistance 60% of the time. The implications of CriticGPT go beyond code quality. It represents a significant step towards better evaluation of complex AI systems - a crucial challenge in AI alignment and safety. What strikes me is OpenAI's consistent focus on innovation. While competitors often claim to have surpassed GPT-4 or SORA, OpenAI quietly continues to produce quality advancements that benefit both the industry and society. Interestingly, CriticGPT's approach reminds me of GAN architecture. Just as generators and discriminators work together to produce realistic images in GANs, we could view GPT-4 as the generator and CriticGPT as the discriminator/critic in this scenario.
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
Aspiring AI/ML Engineer | Kaggler | Open Source Contributor | Hacksquad 2023 Winner | Reflex Hacktoberfest AI Champion | Quine's AI Themed Quest 1st Runner-Up (x2)
We are entering in the era of self improvement in Technology. Let's see what will be the results. Though there is hallucination problem with CriticGPT as listed, but it can be fixed later on, as it out performance individual Human and combination of Human and CriticGPT, alone.
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
super intelligence is coming
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
With powerful model like GPT-4o/Claude 3.5, I'm beginning to question whether writing short Python scripts or creating SQL queries should still be considered technical skills (Especially for building dashboard). It would be fascinating to see how LLMs tackle large-scale coding tasks using something like Opendevin. Has anyone had hands-on experience with these tools for more complex development work? I'm very curious to hear your thoughts and experiences! #AI #Agent #LLM
We’ve trained a model, CriticGPT, to catch bugs in GPT-4’s code. We’re starting to integrate such models into our RLHF alignment pipeline to help humans supervise AI on difficult tasks: https://lnkd.in/gbAue2fZ
