Peering Inside GPT-4: Understanding Its Mixture of Experts (MoE) Architecture
Peering Inside GPT-4: Understanding Its Mixture of Experts (MoE) Architecture
Artificial Intelligence (AI) is rapidly evolving, breaking new ground, and bringing us closer than ever to Artifical General Intelligenceā¦
Artificial Intelligence (AI) is rapidly evolving, breaking new ground, and bringing us closer than ever to Artifical General Intelligence (AGI). OpenAIās GPT-4 is currently the most sophisticated and capable large language model (LLM) and stands head and shoulders above its predecessors in terms of its capabilities and sophistication.
OpenAI hasnāt publicly commented on any of the technical specifications for GPT-4 but itās widely believed to deploy a Mixture of Experts (MoE) model. MoE is an ensemble learning technique that uses multiple specialised models, referred to as āexpertsā, for decision-making. These experts are adept at handling different parts of the input space, a strategy that has proven effective for large and complex data sets. GPT-4ās MoE model is likely to boast 1.76 trillion parameters, an order of magnitude larger than GPT-3, and was released on 14th March 2023.
Fine-Tuning theĀ Experts
GPT-4ās MoE model is believed to house 16 expert models, each with around 111 billion parameters each. Itās likely that at least one expert is geared towards ensuring the safety of the modelās outputs, while another is probably specifically tuned to write code, a key feature that underpins GPT-4ās Code Interpreter plugin.
But what about the other experts? How are they fine-tuned to provide a marked performance improvement over GPT-3.5?
OpenAI has stated that GPT-4 is āmore reliable, creative, and able to handle much more nuanced instructions than GPT-3.5ā. So letās break down how these 16 expert models could be fine-tuned based on the performance improvements weāve seen vs. GPT-3.5:
With GPT-4ās impressive list of improvements and the flexibility that the Mixture of Experts (MoE) model brings, there are several ways in which the expert models could be fine-tuned to further enhance its capabilities. Here is a list of some potential expert models in GPT-4:
Specialised Test Preparation Expert: Given GPT-4ās success in a variety of tests, an expert could be tuned to specialise in preparation for specific tests, understanding specific domains and typical question patterns. Likely areas of fine-tuning are science, law and the general curriculum.
Python Expert: There is undoubtedly an expert model fine-tuned for Python that will underpin many of OpenAIās Code Interpreter plugin as well as GPT-4ās ability to interact with APIs and navigate webpages. This expert will not only generate and understand Python code but also comprehend the specificities of web protocols, HTML, and API responses.
Software Development and Debugging Expert: In addition to generating and debugging code, GPT-4 probably has an expert trained in understanding different programming languages, frameworks, and even specific best practices within software development.
Advanced Image Interpretation Expert: With GPT-4ās new ability to analyse and comment on images, there could be an expert specifically trained to understand different types of images, such as medical imaging (CT, MRI scans), satellite images, architectural plans, or even artwork.
Math and Science Problem Solving Expert: With GPT-4ās prowess at complex problem-solving, there could be experts trained specifically in different scientific disciplines or branches of mathematics.
Data Synthesis and Analysis Expert: To answer complex questions that require synthesising information from multiple sources, an expert could be specially tuned to analyse and extract information from large data sets, academic papers, or extensive documents.
Specialised Fact Checking Expert: Given GPT-4ās increased factual accuracy, an expert could be fine-tuned to be a fact-checker. This model would specialize in verifying information, cross-referencing across multiple sources, and flagging potential inaccuracies.
Safety and Ethics Expert: Given the need for safety in generative AI, an essential expert in the mix would specialise in identifying and moderating outputs that could be biased, offensive, or potentially harmful. This expert will probably have been fine-tuned on specific domain knowledge that OpenAI doesnāt want to be easily accessed by users.
Culture-Specific Expert: To improve multi-lingual capabilities and understand various dialects, an expert model might be fine-tuned with a deep understanding of specific cultures, languages, and their nuances. This model would be adept at recognizing cultural references, idioms, and sentiments, thereby improving overall communication.
Emotional Analysis Expert: To better interpret and generate emotions expressed in text, an expert model could be trained for deep sentiment analysis, helping GPT-4 to understand subtle emotional cues and respond appropriately.
Entertainment and Gaming Expert: With GPT-4ās knack for puzzles, jokes, and brain teasers, there could be an expert that specialises in various forms of entertainment, games, or creative writing.
Quality Management and Self-Reflection Expert: Lastly, this expert would be designed to manage the outputs of all the other expert models. The key role of this model would be to ensure the quality and coherence of the final output. Leveraging self-reflection techniques, this expert would assess the performance of the other experts and their alignment with the expected results. It could be capable of re-evaluating the input and adjusting the weighting of the expert model selection based on the response quality.
Expert Allocation
The big question isāāāhow does GPT-4 choose which experts to consult for a particular request? Itās highly unlikely that all 16 experts are called upon for each task. Instead, a gating network probably selects the most appropriate expert models for the job and the final expert model combines their outputs to produce the final response.
There has been a good amount of news coverage recently about ChatGPT (and GPT-4) losing capability over the last few months. Thereās even research that backs this up with one theory being that additional fine-tuning of GPT-4 has been done to reduce harmful outputs that may have had unintended effects. If any additional fine-tuning has been done over the past few months by OpenAI then I suspect that either be on the Quality Management expert and/or the weights assigned to each expertās output within the overall model. This would make a lot of sense to me as the MoE approach OpenAI has never been used at this scale before and likely needs adjusting as usage scales.
Conclusion
The strides made by OpenAI with GPT-4 serve as a testament to the potential of generative AI technology. The hypothetical breakdown of expert models within GPT-4 provided here is a speculation, but one grounded in the known capabilities and improvements of this impressive language model.
The precise mechanics of expert model selection and task distribution within GPT-4 remains a subject of speculation, and the fine-tuning that OpenAI might be performing to enhance safety and performance is a fascinating topic. The fact that this state-of-the-art model continues to change with additional fine-tuning as usage scales, speaks volumes about the potential for ongoing development and refinement in the field of generative AI.
Iām hopefully that one day Open AI will release more details on GPT-4 (probably when they release GPT-5!) and weāll be able to fully understand all the intricacies of GPT-4 Mixture of Experts. Until then weāll just have to enjoy the impressive array of capabilities it showcases today across various domainsāāāfrom mundane tasks like answering simple queries, to more complex applications like solving intricate mathematical problems or generating high-level code.
This article was researched and written with help from ChatGPT, but was lovingly reviewed, edited and fine-tuned by a human.