Document Optimization: The Key to Efficient Retrieval

AI and retrieval, document expansion has long been seen as a classical technique to enhance retrieval quality. But, ironically, it often ends up cluttering the signal it’s supposed to clarify, especially for modern retrievers.

Rethinking Document Expansion

Instead of sticking with the traditional methods, document expansion is being reinvented as a document optimization challenge. By fine-tuning language models or vision language models, documents are transformed into representations that better match anticipated query distributions. This isn’t just theoretical, using rewards from ranking improvements through GRPO, this approach works across single-vector, multi-vector, and lexical retrievers.

Why does this matter? Because if you can optimize documents efficiently, you shift the heavy computational lifting offline. It's not just saving time. It's making retrieval smarter.

Real-world Impact

Let's put this into perspective with some numbers. Applying this optimization to OpenAI's text-embedding-3-small model, we've seen nDCG5 scores leap from 58.7 to 66.8 for code and from 53.3 to 57.6 in visual document retrieval (VDR). In fact, these results even nudge past the 6.5 times pricier text-embedding-3-large model. If smaller models can outperform the big guns, isn't it time to rethink resource allocation?

when retriever weights are in the mix, document optimization gives fine-tuning a run for its money. Combining both practices, as seen with Jina-ColBERT-V2, led to an impressive jump from 55.8 to 63.3 in VDR and from 48.6 to 61.8 in code retrieval.

The Future of Retrieval

Document optimization is reshaping expectations for AI retrieval systems. For those still throwing vast resources at larger models, show me the inference costs. Then we’ll talk about true efficiency.

The intersection of document transformation and retrieval is becoming undeniable. While many projects are all talk, this isn’t vaporware. It’s a tangible shift towards smarter, leaner AI, not just for researchers, but for industries reliant on AI-driven retrieval.

So, next time you hear about another bloated model promising miracles, ask yourself: Is it optimizing or just expanding aimlessly?