The method in the paper is indeed simple and effective: removing irrelevant information through prompt. But is it necessary to dress up this simple method with a fancy neuroscience term?
Machine Learning
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
I hate that part also at the same time enjoy it.
Well, it's more like a psychological term, and attention is already there to illustrate the intended meaning of a dot product. The analogy holds up, so why doubting the validity of using system 2 attention rather than that of using attention at all?
That was exactly my thought! In Langroid (the agent-oriented LLM framework from ex-CMU/UW-Madison researchers), we call it Relevance Extraction — given a passage and a query, use the LLM to extract only the portions relevant to the query. In a RAG pipeline where you optimistically retrieve top k chunks (to improve recall), the chunks could be large and hence contain irrelevant/distracting text. We concurrently do relevance extraction from these k chunks: https://github.com/langroid/langroid/blob/main/langroid/agent/special/doc_chat_agent.py#L801
One thing often missed in this is the un-necessary cost (latency and token-cost) of parroting out verbatim text from context. In Langroid we use a numbering trick to mitigate this: pre-annotate the passage sentences with numbers, and ask the LLM to simply specify the relevant sentence-numbers. We have an elegant implementation of this in our RelevanceExtractorAgent using tools/function-calling.
Here's a post I wrote about comparing Langroid's method with LangChain's naive equivalent of relevance extraction called `LLMChainExtractor.compress` , and no surprise Langroid's methos is far faster and cheaper:
https://www.reddit.com/r/LocalLLaMA/comments/17k39es/relevance_extraction_in_rag_pipelines/
If I had the time, the next steps would have been: 1. give it a fancy name, 2. post on arxiv with a bunch of experiments, but I'd rather get on with building 😄