Supercharge Your Chatbot in 5 Minutes!

Table of Contents

1. Introduction
2. Fine-Tuning the Policy Model
3. Training the Reward Model
4. Training the Policy Model with PPO
5. Building a Non-Racist and Positive Chat Bot
   • 5.1. Problem Statement
   • 5.2. Using Reinforcement Learning with Human Feedback
   • 5.3. Dataset Preparation
   • 5.4. Fine-Tuning the Policy Model
   • 5.5. Training the Reward Model
   • 5.6. Optimizing the Policy Model with PPO
   • 5.7. Creating a Conversational Chat Bot
   • 5.8. Filtering Harmful Conversation
6. Conclusion

Building a Non-Racist and Positive Chat Bot

Reinforcement learning has become a popular approach in training models for solving business problems. In this article, we will explore how reinforcement learning can be used to build a chatbot that is non-racist and always positive in its responses.

Problem Statement

Imagine You are building a chat bot that interacts with customers. It is essential that the bot behaves in a non-racist manner and always provides positive responses. However, the language models we use to train chat bots often contain racist speech or violent language which can Create a negative impression. To overcome this challenge, we will use reinforcement learning with human feedback to train a model that generates positive responses.

Using Reinforcement Learning with Human Feedback

To train our chat bot, we will follow three steps that we have already discussed in a previous video. The first step is to fine-tune the policy model. The policy model can be any text generation model, such as Falcon, Llama T5, or GPT. In this case, we will use the GPT-IMDB model, which is already fine-tuned on the IMDB dataset. However, if you are using a different dataset, you may need to first fine-tune your chosen model.

The Second step is to train the reward model. Since our objective is to generate positive responses, we can use a sentiment-Based model to classify the responses as positive or negative. In this case, we will use the DistilBERT IMDB sentiment classification model, which can classify the review as positive or negative. We will use a score of 95% for positive responses and 5% for negative responses as the reward for the policy model.

Finally, the third step is to train the policy model with Proximal Policy Optimization (PPO). We will use the query response from the policy model and the sentiment from the reward model to optimize our policy model. By iterating through these steps, we can create a model that only generates positive responses.

Dataset Preparation

To demonstrate the process, let's consider the IMDB dataset, which contains 50,000 movie reviews. The dataset has two columns: review and sentiment. For our purpose, we only require the review column. In the first step, we fine-tune the policy model using the IMDB dataset.

To tokenize our dataset, we use the tokenizer that corresponds to the model we have chosen. In this case, we use the tokenizer from the GPT model. We create a function called build_dataset to tokenize the review column in the IMDB dataset, and we generate input tokens and Attention masks for our model. We also prepare the corresponding labels for the policy model.

Fine-Tuning the Policy Model

After the dataset preparation, we can start fine-tuning the policy model. We define the PPO trainer with the model, the dataset, and the file previously created. We also define the learning rate, size, and tokenizer for our model. Once everything is set up, we tokenize our input data and create a PPU trainer to fine-tune our model.

Training the Reward Model

Next, we train the reward model, which will be used to provide feedback to our policy model. We define a pipeline for the reward model that takes text as input and provides a response with a sentiment level and score. We can pass a sample text to the pipeline, such as "This movie is really bad," and receive the sentiment level as negative and the score as 2.33. This feedback will serve as a reward for optimizing our policy model.

Optimizing the Policy Model with PPO

Now comes the challenging part. We use the policy model from step 1 and the reward model from step 2 to optimize our final model. In the Twinning loop, we follow the three steps we discussed earlier. First, we get the query response from the policy model, which, in our case, is the GPT-2 base model. Then, we pass the query and the response generated by our policy model to the sentiment model, which we defined earlier. The sentiment model provides the level and score for the response generated by our policy model. Finally, we optimize our policy model with PPO using the query-response pair, the reward generated from the sentiment model, and the feedback received.

Creating a Conversational Chat Bot

After completing the optimization step, we can observe a significant improvement in the quality of the generated responses from our policy model. This approach can also be used to create a conversation chat bot that filters out harmful conversation while chatting with the user. By using reinforcement learning with human feedback, we can ensure that the chat bot always generates positive and non-racist responses.

Conclusion

In this article, we explored how reinforcement learning can be used to build a non-racist and positive chat bot. By fine-tuning the policy model, training the reward model, and optimizing the policy model with PPO, we can develop a chat bot that provides positive responses to user queries. This approach helps to filter out harmful conversation and improve the user experience.