DialoGPT: Thế hệ đào tạo sáng tạo cho việc tạo phản hồi trong cuộc trò chuyện

DialoGPT: Thế hệ đào tạo sáng tạo cho việc tạo phản hồi trong cuộc trò chuyện

Table of Contents:

1. Introduction
2. What is GPT?
3. GPT Training Process
4. Preprocessing the Data 4.1 Filtering the Conversations 4.2 Removing Non-Relevant Context 4.3 Dealing with Multiturn Dialogues 4.4 Language Profanity Filtering 4.5 HTML and Coding Removal 4.6 Applying Attention Weights
5. GPT Language Modeling 5.1 Seq2Seq Generation 5.2 Temperature in Sampling 5.3 Context Sensitivity
6. Evaluating GPT Performance 6.1 Human Evaluation 6.2 Automatic Evaluation
7. Fine-Tuning GPT
8. Pros and Cons of GPT
9. Conclusion
10. Frequently Asked Questions (FAQs)

Article:

Introduction

Chào mừng bạn đến với video mới ngày hôm nay, chúng ta sẽ xem xét bài báo có tiêu đề "Hội thoại GPT: Mô hình sinh trắc trước luyện tập rộng và ứng dụng của chúng trong việc tạo ra câu trả lời tổng hợp trong cuộc trò chuyện". Đây là một bài báo từ các nhà nghiên cứu tại Microsoft và đã được chấp nhận vào ACL 2020 trong danh mục Hiện đại hóa Hệ thống. Trước khi tôi bắt đầu video hôm nay, tôi muốn giới thiệu một số thông báo, vì vậy tuần trước tôi đã thăm dò ý kiến ​​của bạn trên tab Cộng đồng xử lý những mối quan ngại về chất lượng âm thanh và video, vì vậy 71% trong số bạn đồng ý, nhưng thật ra không ai đề cập đến vấn đề này là gì. Điều này làm tôi cảm thấy rất bối rối, vậy vấn đề này có thể là gì? Do đó, tôi muốn yêu cầu nếu bạn là người đã đồng ý, vui lòng tiếp tục và viết nó trong phần nhận xét về bản chất của vấn đề này, để tôi có thể sửa nó từ ngày Mai. Nhóm video mà tôi tạo ra cũng nếu bạn không làm như vậy, nếu bạn thấy bài đăng này và tôi muốn khích lệ bạn không thì hãy ghé qua tab Cộng đồng của tôi và để lại tiếng nói của bạn về điều cùng nhau. Hãy quay lại bài báo. Ồ, vậy nên bài viết này sẽ nói về việc sử dụng mô hình GPT để xây dựng các hệ thống trả lời tổng hợp trong cuộc trò chuyện.

What is GPT?

GPT stands for Generative Pre-trained Transformer, and it is a language model released by OpenAI. In this paper, the researchers use the Second version of GPT for pre-training on Chat Data. So, what exactly is GPT and how does it work? GPT is a wide-Scale language model that has been trained on a vast amount of chat data. It is capable of generating coherent responses in a conversational Context during any session. But before we dive into that, for those of You who don't know what GPT is, it is a widely used language model released by OpenAI. So in this paper, they used the second version of it for pre-training on chat data. Thus, the way GPT works in both cases is that it predicts the next word in this sequence by paying Attention to all the words that occurred in the past. Let's assume the time step We Are at is t, and this context makes up the situation we're in. So the model generates a word, and it should follow this context by seeing the words that have already happened in the past. This means that there is a mask applied to the attention weights, and because of that, it can only see the things that have happened on its left and cannot look ahead to anything that happens on its right. This is fundamental and is the difference between BERT and GPT as well. And it continues generating these unique tokens one step at a time until you see some explicit ending to the sequence. So this is the high-level idea of what GPT is and how it works.

GPT Training Process

Now let's move on to more details about the training process of GPT. To begin with, the researchers gathered a massive dataset of conversations, such as exchanges from Reddit comments and subreddits spanning from 2005 to 2017. The model achieves a performance close to humans in terms of both automatic and human evaluations. For single-turn dialogues, they had 147 million conversations, and for multiturn dialogues, they utilized 1.8 billion words. This massive dataset aims to train the model to learn better. Well, that's all the massaging they did to the dataset. Next, they have what they call the source model and the target model. The source model takes the source you have and produces the target. So, for example, if you have a single user, and the second user writes something, then the second user types something else, and let's say this happens for K repetitions. Now, this is the point where your chatbot program needs to intervene. So the input to the GPT model here would be basically all the fractions you see. It's one user, then the second user, then again the response of the user, and so on. And these Patterns are possible so that's what they will extract. And once they've done this pretraining on chat data, the next step is to train the model to generate probabilities with respect to these values, and you want it to learn very well. So basically, what you have is a source and a target, and the model is trained to maximize the probability of producing p for t given x, which means you want the model to be really good at predicting what the subsequent sequences will be given the completion of the source. And in order to be able to tell how well it did, you have to wait until you see a large enough sample to evaluate how performant it is. So they will compute the cross-entropy loss during the generation of S1 or subsequent sentences. They also calculate the Blue score for a few other conditions. So, as you can see, they are pretty confident in terms of how the model-generated output performs.

Preprocessing the Data

Before training the model, the researchers had to preprocess the conversation data to ensure relevance and context coherence. They used several filtering methods:

4.1 Filtering the Conversations: The dataset was filtered by extracting conversations from Reddit comments and subreddits, removing irrelevant or noisy information.

4.2 Removing Non-Relevant Context: Conversations without relevant context were removed to improve the quality and focus of the model's responses.

4.3 Dealing with Multiturn Dialogues: Multiturn dialogues pose a challenge as the model needs to understand the sequence of conversations. The researchers handled this by considering each turn as a separate dialogue to avoid information overlapping.

4.4 Language Profanity Filtering: In order to maintain a safe and appropriate language, profanity filtering was applied to remove offensive content.

4.5 HTML and Coding Removal: HTML tags and coding elements were also removed from the dataset to ensure clean text inputs.

4.6 Applying Attention Weights: Attention weights were utilized to determine the relevance and importance of each word in the conversation, allowing the model to focus on the important context.

These preprocessing steps ensure that the dataset used for training is refined and aligned with the desired context and quality standards.

GPT Language Modeling

The GPT model employs language modeling techniques to generate coherent and contextually relevant responses. Two key components of GPT language modeling are:

5.1 Seq2Seq Generation: GPT uses a sequence-to-sequence generation method, where the model predicts the next word in the sequence Based on the context it has seen so far. It generates the words one by one, taking into account the previous words and trying to anticipate the next word in the sequence.

5.2 Temperature in Sampling: The temperature parameter plays a role in how random or deterministic the model's output will be. A higher temperature value allows for more randomness, while a lower value leads to more deterministic output.

5.3 Context Sensitivity: GPT is context-sensitive and leverages attention mechanisms to understand the previous conversation and generate responses accordingly. It considers the complete conversation history to produce contextually relevant and coherent answers.

The combination of these techniques ensures that the GPT model generates diverse and contextually appropriate responses, maintaining the conversational flow.

Evaluating GPT Performance

The performance evaluation of the GPT model involves both human and automatic evaluations to assess its effectiveness in generating high-quality responses.

6.1 Human Evaluation: Human evaluators assess the quality and coherence of GPT-generated responses. They compare the responses to those generated by humans and rate them based on metrics like relevance, fluency, and overall response quality.

6.2 Automatic Evaluation: Automatic evaluation metrics, such as BLEU scores, are used to measure how well the GPT-generated responses Align with the human-generated responses. These metrics provide quantitative measures of performance.

The combination of human and automatic evaluation assures the researchers and users of the model's reliability and effectiveness in generating Meaningful responses.

Pros and Cons of GPT

Let's take a look at the advantages and disadvantages of using GPT for conversational response generation:

Pros:

• GPT can generate coherent and contextually relevant responses in chat-based applications.
• The model has been trained on a large dataset, allowing it to capture a wide range of conversational patterns.
• GPT can be fine-tuned and customized for specific use cases, increasing its adaptability.
• The automatic evaluation metrics indicate a performance close to human-generated responses.

Cons:

• GPT may generate repetitive or dull responses due to the inherent bias in the training data.
• The language generation process can be computationally intensive and may require substantial computational resources.
• GPT's language modeling approach may not capture the subtleties and nuances of human conversation.
• The model's reliance on the training data may result in biased or inappropriate responses in certain contexts.

Considering these pros and cons, GPT provides a powerful and versatile tool for generating conversational responses but requires careful consideration and monitoring in real-world applications.

Conclusion

In conclusion, the use of GPT for generating conversational responses has shown promising results. By leveraging large-scale pre-training on chat data and fine-tuning the model, GPT demonstrates the ability to produce coherent and contextually appropriate answers. However, it is important to understand the limitations and challenges associated with language models like GPT, such as repetitive responses and potential biases. Continuous research and improvement are essential to enhance the performance and reliability of GPT in various conversational scenarios.

Frequently Asked Questions (FAQs)

Q: What is GPT? A: GPT stands for Generative Pre-trained Transformer, and it is a language model released by OpenAI. It is trained on a vast amount of chat data and can generate coherent responses in conversational contexts.

Q: How does GPT work? A: GPT works by predicting the next word in a given sequence based on the context it has seen so far. It utilizes attention mechanisms and language modeling techniques to generate contextually relevant and coherent responses.

Q: How is GPT trained? A: GPT is trained using a large dataset of conversations, such as Reddit comments and subreddits. The training process involves pre-processing the data, fine-tuning the model, and evaluating its performance using human and automatic evaluation metrics.

Q: What are the advantages of using GPT for conversation generation? A: Some advantages of GPT include its ability to generate coherent responses, its adaptability through fine-tuning, and its performance close to human-generated responses according to evaluation metrics.

Q: Are there any limitations to using GPT? A: Yes, GPT can produce repetitive or dull responses due to training data biases. It may not capture the subtleties of human conversation and can require significant computational resources. Additionally, it may generate biased or inappropriate responses in certain contexts.

Q: How can GPT be improved? A: Continuous research and improvement are required to address the limitations of GPT. This includes refining training data, optimizing the fine-tuning process, and enhancing the model's language generation capabilities.

Q: Can GPT be used in real-world applications? A: Yes, GPT can be used in various chat-based applications to generate conversational responses. However, careful monitoring and consideration are necessary to ensure the quality and appropriateness of the model's output.