{"id":1098456,"date":"2024-11-13T09:30:00","date_gmt":"2024-11-13T17:30:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1098456"},"modified":"2024-11-14T20:53:17","modified_gmt":"2024-11-15T04:53:17","slug":"toward-modular-models-collaborative-ai-development-enables-model-accountability-and-continuous-learning","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/toward-modular-models-collaborative-ai-development-enables-model-accountability-and-continuous-learning\/","title":{"rendered":"Toward modular models: Collaborative AI development enables model accountability and continuous learning"},"content":{"rendered":"\n
\"Modular<\/figure>\n\n\n\n

Today, development of generalizable AI models requires access to sufficient data and compute resources, which may create challenges for some researchers. Democratizing access to technology across the research community can advance the development of generalizable AI models. By applying the core software development concept of modularity to AI, we can build models that are powerful, efficient, adaptable, and transparent. <\/p>\n\n\n\n

Until recently, AI models were primarily built using monolithic architecture. Though powerful, these models can be challenging to customize and edit compared to modular models with easily interpretable functional components. Today, developers employ modularity to make services more reliable, faster to refine, and easier for multiple users to contribute to simultaneously. One promising research direction that supports this involves shifting AI development towards a modular approach (opens in new tab)<\/span><\/a>, which could enhance flexibility and improve scalability. <\/p>\n\n\n\n

One such approach is to use numerous fine-tuned models designed for specific tasks, known as expert models<\/em>, and coordinate them to solve broader tasks (see Towards Modular LLMs by Building and Reusing a Library of LoRAs – Microsoft Research<\/a>, Learning to Route Among Specialized Experts for Zero-Shot Generalization (opens in new tab)<\/span><\/a>). These expert models can be developed in a decentralized way. Similar to the benefits of using a microservice architecture, this modular AI approach can be more flexible, cheaper to develop, and more compliant with relevant privacy and legal policies. However, while substantial research has been done on training optimization, coordination methods remain largely unexplored.<\/p>\n\n\n\n

Our team is exploring the potential of modular models by focusing on two themes: i) optimizing the training of expert models and ii) refining how expert models coordinate to form a collaborative model. One method for coordinating expert models is to adaptively select the most relevant independently developed expert models for specific tasks or queries. This approach, called MoErging<\/em>, is similar to Mixture-of-Experts (MoE) approaches but differs in that the routing mechanism is learned after<\/em> the individual experts are trained. As an initial step, we contributed to creating a taxonomy for organizing recent MoErging methods with the goal of helping establish a shared language for the research community and facilitating easier and fairer comparisons between different methods. <\/p>\n\n\n\n

Assessing existing MoErging methods<\/h2>\n\n\n\n

Most MoErging methods were developed within the past year, so they don\u2019t reference each and are difficult to compare. To enable comparison of MoErging methods, we recently collaborated on a survey<\/a> that establishes a taxonomy for comparing methods and organizes MoErging design choices into three steps: <\/p>\n\n\n\n