Can Compressing Retrieved Documents Boost Language Model Performance? This AI Paper Introduces RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation

Optimizing their performance while managing computational resources is a crucial challenge in an increasingly powerful language model era. Researchers from The University of Texas at Austin and the University of Washington explored an innovative strategy that compresses retrieved documents into concise textual summaries. By employing both extractive and abstractive compressors, their approach successfully enhances the […]

The post Can Compressing Retrieved Documents Boost Language Model Performance? This AI Paper Introduces RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation appeared …

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