3 ms·
> Basically, LLM's already partially encode information as semantic graphs internally. There's an (underutilized?) technique here to take advantage of that int
by CharlieDigital 2y ago
> Basically, LLM's already partially encode information as semantic graphs internally.
There's an (underutilized?) technique here to take advantage of that internal graph: have the LLM tell you the related concepts first and then perform the RAG using not just the original concept, but the expanded set of related concepts.
So:
concept → [related concepts] → [[.. rag-rc1],[.. rag-rc2],[.. rag-rcn]] → summarize
With GPTs prior to 4o, it would have been too slow to do this as a two-step process. With 4o and some of the higher throughput Llama3 based options (Together.ai, Fireworks.ai, Groq.com), a two-step fan-out RAG approach takes advantage of this internal graph and could probably yield similar gains in RAG without additional infrastructure (another datastore) nor data pre-processing to take advantage of a graph approach.
- davedx 2y agoYup. Fascinating stuff really. Kind of like mnemonics for LLM's, if you squint a bit.
- neeleshs 2y agoEven with old GPT, if the summary is decent, it works reasonably well with even no RAG. We are a data management platform and allow users to build data pipelines around a data model. This is basically a DAG. We autogenerate documentation for these pipelines, using gpt4, and feed a summarized version of the data pipeline - expressed as graphviz dot file format in the prompt. gpt4 understands this format well, and seeminlgy understands the graph itself reasonably well! It performs poorly expressing the higher level intent of the pipeline, but tactical details are accurately documented. We are trying to push prompting itself more, before turning to RAG & finetuning