Superior Retrieval Methods in a World of 2M Token Context Home windows, Half 1 | by Meghan Heintz | Jul, 2024

Exploring RAG strategies to enhance retrieval accuracy

Gemini Professional can deal with an astonishing 2M token context in comparison with the paltry 15k we had been amazed by when GPT-3.5 landed. Does that imply we not care about retrieval or RAG programs? Primarily based on Needle-in-a-Haystack benchmarks, the reply is that whereas the necessity is diminishing, particularly for Gemini fashions, superior retrieval strategies nonetheless considerably enhance efficiency for many LLMs. Benchmarking outcomes present that lengthy context fashions carry out effectively at surfacing particular insights. Nonetheless, they battle when a quotation is required. That makes retrieval strategies particularly vital to be used instances the place quotation high quality is vital (assume legislation, journalism, and medical purposes amongst others). These are typically higher-value purposes the place missing a quotation makes the preliminary perception a lot much less helpful. Moreover, whereas the price of lengthy context fashions will doubtless lower, augmenting shorter content material window fashions with retrievers is usually a cost-effective and decrease latency path to serve the identical use instances. It’s secure to say that RAG and retrieval will stick round some time longer however perhaps you gained’t get a lot bang on your buck implementing a naive RAG system.

Superior RAG covers a variety of strategies however broadly they fall underneath the umbrella of pre-retrieval question rewriting and post-retrieval re-ranking. Let’s dive in and be taught one thing about every of them.

Q: “What’s the that means of life?”

A: “42”

Query and reply asymmetry is a large difficulty in RAG programs. A typical method to easier RAG programs is to check the cosine similarity of the question and doc embedding. This works when the query is almost restated within the reply, “What’s Meghan’s favourite animal?”, “Meghan’s favourite animal is the giraffe.”, however we’re hardly ever that fortunate.

Listed below are just a few strategies that may overcome this:

The nomenclature “Rewrite-Retrieve-Learn” originated from a paper from the Microsoft Azure group in 2023 (though given how intuitive the method is it had been used for some time). On this examine, an LLM would rewrite a consumer question right into a search engine-optimized question earlier than fetching related context to reply the query.

The important thing instance was how this question, “What career do Nicholas Ray and Elia Kazan have in frequent?” must be damaged down into two queries, “Nicholas Ray career” and “Elia Kazan career”. This permits for higher outcomes as a result of it’s unlikely {that a} single doc would comprise the reply to each questions. By splitting the question into two the retriever can extra successfully retrieve related paperwork.

Rewriting may also assist overcome points that come up from “distracted prompting”. Or situations the place the consumer question has combined ideas of their immediate and taking an embedding immediately would end in nonsense. For instance, “Nice, thanks for telling me who the Prime Minister of the UK is. Now inform me who the President of France is” can be rewritten like “present French president”. This can assist make your utility extra sturdy to a wider vary of customers as some will assume lots about the way to optimally phrase their prompts, whereas others may need totally different norms.

In question enlargement with LLMs, the preliminary question may be rewritten into a number of reworded questions or decomposed into subquestions. Ideally, by increasing the question into a number of choices, the possibilities of lexical overlap improve between the preliminary question and the right doc in your storage part.

Question enlargement is an idea that predates the widespread utilization of LLMs. Pseudo Relevance Suggestions (PRF) is a method that impressed some LLM researchers. In PRF, the top-ranked paperwork from an preliminary search to determine and weight new question phrases. With LLMs, we depend on the inventive and generative capabilities of the mannequin to search out new question phrases. That is useful as a result of LLMs are usually not restricted to the preliminary set of paperwork and may generate enlargement phrases not lined by conventional strategies.

Corpus-Steered Query Expansion (CSQE) is a technique that marries the normal PRF method with the LLMs’ generative capabilities. The initially retrieved paperwork are fed again to the LLM to generate new question phrases for the search. This method may be particularly performant for queries for which LLMs lacks topic data.

There are limitations to each LLM-based question enlargement and its predecessors like PRF. Probably the most obvious of which is the belief that the LLM generated phrases are related or that the highest ranked outcomes are related. God forbid I’m looking for details about the Australian journalist Harry Potter as a substitute of the well-known boy wizard. Each strategies would additional pull my question away from the much less well-liked question topic to the extra well-liked one making edge case queries much less efficient.

One other technique to cut back the asymmetry between questions and paperwork is to index paperwork with a set of LLM-generated hypothetical questions. For a given doc, the LLM can generate questions that may be answered by the doc. Then through the retrieval step, the consumer’s question embedding is in comparison with the hypothetical query embeddings versus the doc embeddings.

Which means that we don’t have to embed the unique doc chunk, as a substitute, we will assign the chunk a doc ID and retailer that as metadata on the hypothetical query doc. Producing a doc ID means there may be a lot much less overhead when mapping many questions to 1 doc.

The clear draw back to this method is your system shall be restricted by the creativity and quantity of questions you retailer.

HyDE is the alternative of Hypothetical Question Indexes. As a substitute of producing hypothetical questions, the LLM is requested to generate a hypothetical doc that may reply the query, and the embedding of that generated doc is used to look towards the true paperwork. The true doc is then used to generate the response. This methodology confirmed sturdy enhancements over different modern retriever strategies when it was first launched in 2022.

We use this idea at Dune for our pure language to SQL product. By rewriting consumer prompts as a doable caption or title for a chart that might reply the query, we’re higher in a position to retrieve SQL queries that may function context for the LLM to jot down a brand new question.