Aditi Tuli

AI and human behavior @Stanford

[Aditi Tuli @aditituli_]

Excited to introduce RAPTOR at #ICLR2024!

RAPTOR is a tree based retrieval system that can navigate between low-level details and a holistic understanding of texts. Check out our paper here: https://t.co/v60wILm4VT 🦖

[Parth Sarthi @parthsarthi03]

Looking for a RAG system that navigates between granular details and the big picture?

We’re excited to introduce RAPTOR, a tree-based retrieval approach that sets a new SoTA on 3 benchmarks #ICLR2024

w/@salmanabdullah_ , @aditituli_, @shubhkhanna__, @annadgoldie & @chrmanning at @stanfordNLP
https://t.co/RfgK4ZNwh3
🧵

[Mark Tenenholtz @marktenenholtz]

RAPTOR is a retrieval method that treats documents like hierarchies.

Instead of just matching to chunks of text, RAPTOR finds the most similar passages to your query and then traverses the document like a tree to find the most relevant high-level context and low-level details.

[Jerry Liu @jerryjliu0]

RAPTOR by @parthsarthi03 is a cool new paper on tree-structured-retrieval 🌲 that harkens back to the early days of @llama_index / gpt_index.

Been meaning to dig into it, in the meantime @ravithejads has a great summary below 👇

[AK @_akhaliq]

Stanford presents RAPTOR

Recursive Abstractive Processing for Tree-Organized Retrieval

paper page: https://t.co/6Gfcm11Bwx

Retrieval-augmented language models can better adapt to changes in world state and incorporate long-tail knowledge. However, most existing methods retrieve only short contiguous chunks from a retrieval corpus, limiting holistic understanding of the overall document context. We introduce the novel approach of recursively embedding, clustering, and summarizing chunks of text, constructing a tree with differing levels of summarization from the bottom up. At inference time, our RAPTOR model retrieves from this tree, integrating information across lengthy documents at different levels of abstraction. Controlled experiments show that retrieval with recursive summaries offers significant improvements over traditional retrieval-augmented LMs on several tasks. On question-answering tasks that involve complex, multi-step reasoning, we show state-of-the-art results; for example, by coupling RAPTOR retrieval with the use of GPT-4, we can improve the best performance on the QuALITY benchmark by 20% in absolute accuracy.