Beyond Semantic Similarity

When I implemented retrieval in our production system a few months ago, one of the most important benchmarks was cross-language retrieval (query in one language, documents in another), which is a common situation in large enterprises (headquarters + branches). I suspect their idea will perform poorly if the source language and the target language are too different from one another, like English and Hindi (grep often will not return anything).

Another requirement was keeping latency as low as possible (we managed to get Interesting research anyway, but I'd still stick with embedding/reranker-based retrieval (+BM25 for hybrid search) because you do not waste time wandering around blindly each time, trying to find the minimal context to start from, which could have been found immediately with an index. Another issue is that research papers often implement subpar baselines for the approaches they compare against. When I was implementing retrieval, the straightforward implementation gave me 40% accuracy, and various tricks/parameter tuning pushed it to 85%+ without changing the overall architecture (took about a month of experimentation).

Map-reduce as a pattern might be on its way back. Hear me out. High localization wins even when coverage is not super great -- just map shards of the corpus and reduce the learnings. Rinse and repeat, do as many rounds of map and reduce to traverse the corpus until converge. This can also work well when the cluster is combined with different agents, they are tasks equally by prompts anyway.

It depends on your data, as well as what you are trying to optimize for: speed, cost, precision, etc.

In many cases cheap methods like grepping and BM25 just are not going to work well, so semantic similarity is the best initial retriever/filter, followed by LLM-as-judge as a second filter/reranker if you need the precision.

Maybe the best "index" will just be markdown files fed into a tiny LLM model.

Is anyone using small, low-latency, fast LLMs to implement stuff like search as a RAG alternative? Could be the perfect use case for that Llama3 8B ASIC some company showed off a few months ago.

I think it works great for most cases. For example, everyone at work uses coding agents and I never heard anyone say that the agent missed something in terms of retrieval (grepping in this case).

But current IR methods both lexical and semantic retrieval definitely have bottlenecks as pointed out in the the obliq-bench paper (https://arxiv.org/abs/2605.06235).