RAPTOR: Enhancing Language Models with Hierarchical Context Retrieval

Retrieval-augmented language models have an issue of only retrieving short chunks from a corpus, limiting the overall document context. This is a significant issue, as it reduces their ability to adapt to changes in the world state and incorporate long-tail knowledge. Researchers from Stanford University propose RAPTOR, an innovative indexing and retrieval system designed to address these limitations.

RAPTOR utilizes a tree structure to capture a text’s high-level and low-level details. It clusters text chunks, generates summaries for clusters, constructs a tree from the bottom up, and enables the efficient and effective answering of questions at various levels.

The key contribution of RAPTOR is using text summarization for retrieval augmentation, enhancing context representation across different scales and addressing reading semantic depth and connection issues by constructing a recursive tree structure.

RAPTOR outperforms baseline methods across three question-answering datasets: NarrativeQA, QASPER, and QuALITY. Control comparisons show consistent superiority of RAPTOR over traditional methods, establishing new benchmarks in various question-answering tasks. This innovative tree-based retrieval system is a promising approach for advancing the capabilities of language models through enhanced contextual retrieval.

Don’t forget to follow the project on Twitter and Google News and check out the original paper for more details on RAPTOR. Overall, RAPTOR proves to be a promising approach for advancing the capabilities of language models through enhanced contextual retrieval.

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