Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
Authors: Xin Cheng, Xun Wang, Xingxing Zhang, Tao Ge, Si-Qing Chen, Furu Wei, Huishuai Zhang, Dongyan Zhao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that x RAG achieves an average improvement of over 10% across six knowledge-intensive tasks, compatible with various language model backbones, ranging from a dense 7B model to an 8x7B Mixture of Experts configuration. |
| Researcher Affiliation | Collaboration | 1 Peking University 2 Microsoft 3 National Key Laboratory of General Artificial Intelligence |
| Pseudocode | No | The paper describes the x RAG architecture and training strategy in text and diagrams, but it does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/Hannibal046/x RAG. |
| Open Datasets | Yes | Natural Questions [41], Trivia QA [33], and Web Questions [8]...Hotpot QA [81]...Truthful QA [50]...Fact KG [39]. |
| Dataset Splits | No | The paper specifies training datasets and test datasets for evaluation but does not explicitly detail the use of a separate validation dataset or its splits. |
| Hardware Specification | Yes | These experiments were performed on the same computational hardware, specifically an Nvidia A100 and an AMD EPYC 7V12 64-Core Processor. |
| Software Dependencies | No | The paper mentions several models and tools like Mistral-7b, Mixtral-8x7b, SFR, ColBERT-v2, and Torch Profiler, but it does not specify their version numbers or other software dependencies with version details. |
| Experiment Setup | Yes | In Table 9 and Table 10, we list the hyperparameters for Paraphrase Pretraining and Context-aware Instruction Tuning. |