Exploring Retrieval-Augmented Generation (RAG) for Baby Bliss Bot

To improve communication for Blissymbolics users, ongoing work is exploring the integration of Retrieval-Augmented Generation (RAG) into the Baby Bliss Bot project. RAG is an advanced AI technique designed to enhance the accuracy of generative models by incorporating factual knowledge from external sources. This approach helps to overcome limitations in traditional language models, which may sometimes generate responses that are inaccurate or inconsistent due to relying solely on their training data.

During a collaborative session with an Augmentative and Alternative Communication (AAC) user, the potential of RAG became evident. For instance, when the user expressed interest in inviting “Roy nephew” to a birthday party, uncertainties arose about whether “Roy” and “nephew” referred to the same person or different individuals. Traditional models might interpret this query differently in different contexts, leading to confusion.

RAG addresses such challenges by using external vector databases to retrieve information about the user’s family members and their relationships. By integrating this context into prompts, language models can provide more accurate and contextually appropriate answers to user queries.

The ongoing exploration of RAG for Baby Bliss Bot represents an advancement in technology at improving communication accessibility for Blissymbolics users. Moving forward, researchers plan to further refine RAG’s capabilities to ensure it can handle a wide range of queries accurately and effectively, thereby enhancing user experience and usability.