IBM STAIR RAG paper
Great RAG paper from IBM. There are some really good ideas on how to solve common RAG issues. It's...
STAIR improves RAG performance significantly while reducing hallucinations, offering a practical solution for developers working with structured documents.
IBM's STAIR paper addresses RAG issues by using a table of contents as an addressing scheme for generative retrieval. On SearchTome benchmark, STAIR achieves Recall @1 of 82.6%, outperforming fine-tuned Differentiable Search Index at 76.9%, BM25 at 59.5%, and DPR at 68.7%. Hallucination remains below 0.05%, and the method generalizes well with minimal training samples.
Great RAG paper from IBM. There are some really good ideas on how to solve common RAG issues. It's...
Great RAG paper from IBM. There are some really good ideas on how to solve common RAG issues. It's well known that retrievers chunk long documents by length, which discards the hierarchy the document already has. So they propose using a table of contents. A table of contents helps to encodes exactly the global structure that chunking throws away. STAIR uses that table of contents as the addressing scheme for a generative retriever, so the model stores and retrieves information from its own parameters against a structure the corpus supplies. On SearchTome, it reaches Recall @1 of 82.6 percent against 76.9 percent for a fine-tuned Differentiable Search Index, a statistically significant gap, with BM25 at 59.5 percent and DPR at 68.7 percent. Hallucination stays below 0.05 percent, which is the standing objection to generative retrieval and the reason grounding the address space in a real hierarchy is worth the extra structure. The ablations also show it generalizes where very few training samples exist. Paper: academy.dair.ai/papers/stair-s… 💬 4 🔄 0 ❤️ 9 👀 1357 📊 6 ⚡
