MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications

TL;DR

MATE is a flexible, privacy-preserving multi-agent system that uses LLMs and ML classifiers to convert data into accessible formats, aiding users with disabilities across various domains.

Contribution

The paper introduces MATE, a multimodal accessibility multi-agent system that supports customizable modality conversions and integrates a novel model, ModCon-Task-Identifier, for precise task extraction.

Findings

ModCon-Task-Identifier outperforms other models on custom data

MATE effectively converts data to accessible formats in real-time

System is adaptable to various hardware and privacy-sensitive environments

Abstract

Accessibility remains a critical concern in today's society, as many technologies are not developed to support the full range of user needs. Existing multi-agent systems (MAS) often cannot provide comprehensive assistance for users in need due to the lack of customization stemming from closed-source designs. Consequently, individuals with disabilities frequently encounter significant barriers when attempting to interact with digital environments. We introduce MATE, a multimodal accessibility MAS, which performs the modality conversions based on the user's needs. The system is useful for assisting people with disabilities by ensuring that data will be converted to an understandable format. For instance, if the user cannot see well and receives an image, the system converts this image to its audio description. MATE can be applied to a wide range of domains, industries, and areas, such as healthcare, and can become a useful assistant for various groups of users. The system supports multiple types of models, ranging from LLM API calling to using custom machine learning (ML) classifiers. This flexibility ensures that the system can be adapted to various needs and is compatible with a wide variety of hardware. Since the system is expected to run locally, it ensures the privacy and security of sensitive information. In addition, the framework can be effectively integrated with institutional technologies (e.g., digital healthcare service) for real-time user assistance. Furthermore, we introduce ModCon-Task-Identifier, a model that is capable of extracting the precise modality conversion task from the user input. Numerous experiments show that ModCon-Task-Identifier consistently outperforms other LLMs and statistical models on our custom data. Our code and data are publicly available at https://github.com/AlgazinovAleksandr/Multi-Agent-MATE.