ChatGPT Architecture: Will ChatGPT Replace Search Engine?
The emergence of ChatGPT AI architecture is expected to revolutionize internet search. With natural language processing and machine learning algorithms, ChatGPT can provide more accurate and personalized results compared to traditional search engines. ChatGPT enables users to ask questions and receive real-time responses from an AI-powered chatbot.
The potential impact of ChatGPT on web searches is huge. It has several advantages over other search engine technologies, including faster response times, greater accuracy as it can understand natural language queries, improved personalization by comprehending user preferences and context, and enhanced security due to its encrypted interactions.
Moreover, ChatGPT's interactive AI platform can handle more complex tasks like customer service inquiries and technical support requests that would otherwise require human intervention or manual research by employees or customers. This capability can help businesses that heavily rely on customer interaction via digital channels such as websites and mobile apps.
This article will discuss the following topics:
- Introduction to ChatGPT
- Technical Principles of ChatGPT
- Whether ChatGPT has the potential to replace traditional search engines such as Google.
Introduction Of ChatGPT
ChatGPT, an intelligent conversational system based on the GPT-3.5 architecture, has become immensely popular in recent days, creating a buzz in the tech community and inspiring people to share ChatGPT-related content and test examples online. The results are impressive, and the last time we saw such a phenomenon was when GPT-3 was released in the field of natural language processing over two and a half years ago. While the heyday of artificial intelligence seems like a distant memory now, ChatGPT has taken up the torch in the AIGC (AI-generated content) category and is currently a lifesaver for AI during this low period after the bubble burst.
Models like DaLL E2 and Stable Diffusion have been representing the Diffusion Model in the multimodal domain, which has been popular for the past half-year with AIGC models. ChatGPT belongs to this category and has taken the AI industry by storm.
While there are numerous examples of ChatGPT's capabilities online, in this article, we will focus on the technology behind it and how it achieves its extraordinary results. One question that comes to mind is whether ChatGPT can replace search engines like Google. We will explore this question in detail.
It's worth noting that the opinions shared in this article are subjective and may be biased. Let's begin by examining the methods employed by ChatGPT to achieve its remarkable results.
The Technical Principles of ChatGPT
ChatGPT is a sophisticated language model built on the GPT-3.5 architecture, which employs a unique combination of reinforcement learning and human-annotated data (RLHF). This approach enables ChatGPT to continually improve its pre-trained language model by understanding and interpreting human language commands, including generating short essays, answering knowledge-based questions, brainstorming ideas, and more.
The ultimate objective of ChatGPT is to evaluate the quality of responses to user queries based on several criteria, including relevance, depth of information, usefulness, safety, and impartiality.
The ChatGPT training process is divided into three stages, which involve the utilization of human-annotated data and reinforcement learning. These stages are as follows:
ChatGPT: Initial Phase
During the initial phase, ChatGPT employs a supervised policy model to address the cold start problem. Although GPT-3.5 is a powerful language model, it struggles with understanding the various nuances and intentions behind different types of human commands, as well as determining the quality of the generated content. To provide GPT-3.5 with a preliminary understanding of human commands' intentions, a batch of commands or questions submitted by test users is randomly selected and professionally annotated to provide high-quality answers for the specified prompts.
These annotated data are then used to fine-tune the GPT-3.5 model. This process enables ChatGPT to acquire an initial ability to comprehend human prompts' underlying intentions and provide relatively high-quality responses based on those intentions. However, this is merely a starting point, and more work needs to be done to improve ChatGPT's performance.
ChatGPT: Stage Two
The primary objective of the second stage is to develop a reward model (RM) through the use of manually annotated training data. This stage involves the random selection of a batch of user-submitted requests, which are predominantly the same as those in the first stage. The cold start model, which was fitted in the first stage, is then utilized to generate K different responses for each request. Consequently, the model produces a dataset.
The annotator then assesses the K results based on several criteria such as relevance, informativeness, dangerousness, etc. and arranges them in a specific order. This ranking represents the manually annotated data for this stage.
Subsequently, the ordered data is used to train a reward model through the common peer-to-peer learning method for ranking. By combining the results ordered by K, we form $\binom{k}{2}$ pairs of training data. ChatGPT utilizes a pairwise loss function to train the reward model. The RM model receives input and generates a score to evaluate the answer's quality.
For each training data pair, we assume that answer1 precedes answer2 in manual classification. As a result, the loss function encourages the RM model to produce a higher score than the former.
In summary, in this phase, the supervised policy model generates K results for each request after the cold start. The results are manually ranked in descending order of quality and used as training data to train the reward model using the learning pairwise ranking method. The trained RM model receives input and generates the quality score of the result, with a higher score indicating a higher quality of the response generated.
ChatGPT: Third stage
During the third phase of ChatGPT's training process, reinforcement learning is employed to enhance the model's capabilities. In this stage, there is no need for manual annotation data. Instead, the RM model trained in the previous phase is utilized to update the parameters of the pre-trained model based on the RM scoring outcomes.
In this phase, a batch of new commands is randomly sampled from the user's prompts, which differ from those used in the first and second stages. The cold start model initializes the parameters of the PPO model, and for the selected prompts, the PPO model generates responses. The RM model then provides a reward score to evaluate the quality of the responses. The reward score is based on the overall quality of the complete response, which consists of a sequence of words. The reward is transmitted from back to front for each word, generating a policy gradient that updates the parameters of the PPO model.
By iterating through the second and third phases, the LLM model becomes more capable as the RM model is improved in the second phase using manually annotated data, and in the third phase, the improved RM model scores responses to new prompts more accurately and uses reinforcement learning to teach the LLM model to produce high-quality responses that meet RM standards. This approach expands high-quality training data, thus further improving the LLM model. The second and third phases have a mutual promotion effect, leading to sustained improvement with continuous iteration.
While reinforcement learning is used in the third phase to animate the LLM model, it may not be the primary reason why ChatGPT works so well. Suppose a different method is used in the third phase, where the cold start model generates k responses, which are scored by the RM model, and the highest scored response is chosen to refine the LLM model. This approach may be comparable to reinforcement learning in terms of effectiveness, although it is not as sophisticated. Regardless of the technical mode adopted in the third phase, it essentially uses the RM model learned in the second phase to expand high-quality training data for the LLM model.
The ChatGPT model is an improved instructGPT, mainly differing in the annotated data collection method. The model structure and training process follow instructGPT. It is expected that the use of reinforcement learning from human feedback will quickly spread to other content generation fields, such as machine translation models. However, adopting this technology in a specific field of NLP content generation may not be significant, as ChatGPT itself can handle a wide range of tasks, covering many sub-fields of NLP generation. If applied to other fields, such as image, audio, and video generation, it may be a direction worth exploring.
Whether ChatGPT Can Replace Traditional Search Engines Like Google
Can ChatGPT replace traditional search engines like Google? This question arises because of ChatGPT's impressive ability to answer a wide range of prompts. However, it is currently not feasible for ChatGPT to fully replace search engines like Google, although with some technical modifications, it could be possible in the future.
There are three primary reasons why ChatGPT cannot replace traditional search engines currently. First, ChatGPT can provide answers that appear reasonable but are actually incorrect for many knowledge-related questions. This can be confusing for users who may not know whether to trust the results. This issue could be fatal for ChatGPT as a replacement for traditional search engines.
Secondly, ChatGPT's current model is not designed to absorb new knowledge easily. As new knowledge emerges, it is unrealistic to retrain the model every time. Fine-tuning is a feasible and low-cost option, but frequent fine-tuning can lead to forgetting of the original knowledge, leading to disastrous consequences. The challenge of integrating new knowledge into ChatGPT real-time is significant.
Thirdly, the training and online inference costs of ChatGPT are too high, making it difficult to handle the millions of user requests that traditional search engines handle. If OpenAI continues to offer its services for free, it would be challenging to sustain the cost. Charging users would reduce the user base significantly, creating a dilemma.
Although these problems exist, it is possible to solve them. The technical route of ChatGPT can be used as the main framework and combined with existing technical means used by other dialogue systems to modify ChatGPT. By incorporating abilities such as evidence display of the generated results based on retrieval results and the adoption of the retrieval mode for new knowledge introduced by the LaMDA system, new knowledge can be introduced and the credibility of the generated content can be verified in real-time. Except for the cost issue, the first two technical issues mentioned above can be resolved.
Comments