There has been a growing need for natural language processing(NLP) technology that can support engaging and more effective conversations, as a huge number of people are turning to chatbots and other conversational AI systems to help with tasks and answer questions.
ChatGPT is one of the latest developments and it is a variant of the popular GPT-3 language model that is designed specifically for conversational modeling.
ChatGPT is a deep learning model and to generate human-like text, it uses a transformer-based network. It allows the model to process input text and generate responses in a way that is similar to human responses. However, unlike traditional language models, ChatGPT is designed specifically and fine-tuned to support conversational modeling, which means it can generate responses that are relevant and more coherent in the context of a conversation.
ChatGPT:
One of the main challenges in building a conversational AI system is the ability to generate responses that are both coherent and relevant. In other words, the system should be able to generate responses that make sense in the context of the conversation and are on-topic. The struggle with traditional language is that they may generate responses that are technically correct but don’t contribute to the conversation in a meaningful way.
On the other hand, ChatGPT can generate responses that are relevant and coherent. ChatGPT can learn the patterns and nuances of natural conversion and generate responses that are to be coherent and relevant, by using a large dataset of conversation text as its training data.
The ability to maintain the flow of the conversation and prevent it from becoming stilted and repetitive is another challenge in building a conversational AI system. By integrating some techniques to help it generate varied and more natural responses, ChatGPT addresses this problem. For example, it can generate multiple potential responses to a given prompt and based on the context of the conversation choose the most suitable one. This assists to prevent the system from generating repetitive or nonsensical responses and keeps the conversation flowing smoothly.
Overall, ChatGPT has the potential to revolutionize the way we interact with chatbots and other conversational systems and represents a significant advance in the field of conversational AI. ChatGPT can help to make more engaging and natural conversation, covering the way for more effective and more conversational AI with its ability to generate coherent and relevant responses.
How does it work?
Looking at translation models is a good analogy to understand how the language model works. They are composed of 2 main parts encoder and decoder. The encoder encodes the input like in french into the numeric representation called an embedding. Then the numeric representation is given to the decoder where it is decoded into a target language like English.
ChatGPT uses the decoder part of the transformer architecture as exists in GPT-3 in autoregressive form. It means, it is optimized to predict the next word in a sequence. one of the largest problems while using the own output of a model as input is that it can cause unintended and unpredictable behaviors. That’s why GPT3 makes up facts often, produces biased text, or doesn’t follow the prompt of the user properly.
Reinforcement learning from Human feedback:
The method has three steps:
- Supervised fine-tuning step: a pre-trained language model is fine-tuned on a relatively small amount of demonstrated data curated by labelers, to learn a supervised policy(SFT model) that produces outputs from a selected list of prompts. It refers to the baseline model.
- “Mimic Human Preferences” step: labelers are asked to vote on a large number of the SFT model outputs, this way making a new dataset including comparison data. A new model is trained on this dataset. This is defined as the reward model(RM).
- Proximal Policy Optimization(PPO) step: for further fine-tuning and improving the SFT model, the reward model is used.
Step 1 takes place only once and steps 2 and 3 can be integrated continuously. More comparison data is collected on the current best policy model, which is used to train a new reward model and a new policy.
Conclusion:
ChatGPT has not replaced people’s jobs. It is still a long way from being able to replace the creative and interpersonal skills of human beings, but it is a powerful tool for automating conversations. GPT technology is used to enhance tasks, not replace them. If used correctly, it can free up more time to focus on higher-value tasks that need more creativity and problem-solving for people.
