Today we're discussing an exciting new development from OpenAI - ChatGPT, a chatbot trained to interact with users in a conversational way. We'll be taking a quick look into the methods behind ChatGPT and discuss its limitations.
This blog post is available as a video on our YouTube channel.
Methods
A few words about how ChatGPT works. OpenAI developed ChatGPT using a technique called Reinforcement Learning from Human Feedback (RLHF), which involves supervised fine-tuning and human AI trainers providing conversations playing both sides - the user and the AI assistant. To create a reward model for reinforcement learning, OpenAI collected comparison data by having AI trainers rank model-written responses to conversations. They then fine-tuned the model using Proximal Policy Optimization and several iterations of this process. ChatGPT was ultimately fine-tuned from a model in the GPT-3.5 series, which was trained on Azure AI supercomputing infrastructure.
Results
ChatGPT has achieved impressive results, including being able to answer complex questions about the world, generate and solve coding questions, write creative text, give clarifications, and more. In one impressive example available on the OpenAI website, ChatGPT provided a fluent description of a theorem and answered follow-up questions about its use in cryptography. There are a lot of examples in the original blog post that introduced ChatGPT.
Limitations
While ChatGPT is impressive in many ways, it does have some limitations. It can sometimes produce plausible-sounding but incorrect or nonsensical answers, and is sensitive to changes in input phrasing or attempting the same prompt multiple times. It can also be excessively verbose and overuse certain phrases, and may exhibit biased behavior. Ideally, the model would ask clarifying questions when the user provides an ambiguous query, but currently it usually guesses what the user intended.
Iterative Deployment
OpenAI is aware of these limitations and is working to improve its AI systems. ChatGPT is just one step in their iterative deployment of AI systems. They have already made significant reductions in harmful and untruthful outputs using reinforcement learning techniques that leverage human feedback. In the future, they hope to deploy models that can learn from human feedback in real-time and adapt to changing environments.
Significance of ChatGPT for businesses
Here are Some possible applications of ChatGPT for businesses include:
- Customer service: ChatGPT could be used to provide quick and accurate responses to customer inquiries, freeing up human customer service representatives to focus on more complex tasks.
- Lead generation: ChatGPT could be used to engage with potential customers, collecting information and qualifying leads.
- Content creation: ChatGPT's ability to generate creative text could be useful for businesses in creating marketing materials, such as social media posts or website content.
- Data analysis: ChatGPT could be used to analyze and extract insights from large volumes of data, such as customer feedback or market trends.
Overall, ChatGPT has the potential to improve efficiency and productivity for businesses by automating certain tasks and providing valuable insights. It's important to note, however, that ChatGPT is not a perfect tool and may still have limitations that businesses need to consider when deciding how to use it.
Conclusion
ChatGPT is a very exciting development in the field of conversational AI. The quality of the model is extremely impressive (give it a try yourself here), and there are tons of areas where the model can potentially already deliver significant business value. Of course, it's not perfect, and there are still areas for improvement. We look forward to seeing how the field of LLMs will continue to evolve over the coming years.
If you are looking to develop cutting-edge projects that leverage large language models such as GPT / ChatGPT, we at The Global NLP Lab would be happy to assist you on your AI journey. Get in touch to find out how we could help you!
