Summary of Raptor: Recursive Abstractive Processing For Tree-organized Retrieval, by Parth Sarthi et al.
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
by Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, Christopher D. Manning
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract introduces a novel approach to retrieval-augmented language models that can better adapt to changes in world state and incorporate long-tail knowledge. The method, called RAPTOR, recursively embeds, clusters, and summarizes chunks of text, constructing a tree with differing levels of summarization from the bottom up. At inference time, RAPTOR retrieves from this tree, integrating information across lengthy documents at different levels of abstraction. The authors demonstrate significant improvements over traditional retrieval-augmented LMs on several tasks, including question-answering tasks that involve complex, multi-step reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to help language models understand more about the world and learn from long texts. This approach is called RAPTOR. It works by breaking down text into smaller pieces, grouping similar parts together, and then summarizing each group. This helps the model learn more about the context of longer documents. The authors tested RAPTOR and found it outperformed other models on several tasks, including answering questions that require thinking through multiple steps. |
Keywords
* Artificial intelligence * Inference * Question answering * Summarization
