Enhancing Frame Detection with Retrieval Augmented Generation

I usually annotate my chunks with title, summary, keywords and multiple levels of hierarchical topics. More recently I thought about annotating intent, user values and tactics, especially for debate related text and LLM chats. So I would annotate state (the summary of the content itself), values (intent and values) and policy (tactics), taking inspiration from RL.

The idea of detecting frames and using them to tease out the implicit meaning from text is quite nice. It seems there is a lot more to discover about using LLMs prior to RAG. Text is like code, you can't know what it does untill you run it, and in this case, until you annotate it. For example "10+10" won't embed close to "20". And "The fifth letter in this string" won't retrieve "f" by emmbedding similarity.

Cool paper but unsurprising results since anything benefits from RAG