Summary of Adacomp: Extractive Context Compression with Adaptive Predictor For Retrieval-augmented Large Language Models, by Qianchi Zhang and Hainan Zhang and Liang Pang and Hongwei Zheng and Zhiming Zheng
AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models
by Qianchi Zhang, Hainan Zhang, Liang Pang, Hongwei Zheng, Zhiming Zheng
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed paper introduces AdaComp, a low-cost extractive context compression method for enhancing the accuracy and efficiency of Retrieval-Augmented Generation (RAG) systems. This method adaptively determines the compression rate based on query complexity and retrieval quality. The authors first annotate the minimum top-k documents necessary for RAG to answer a query as the compression rate, then construct triplets of queries, retrieved documents, and compression rates. A predictor is trained using this triplet dataset. Experimental results on four QA datasets show that AdaComp significantly reduces inference costs while maintaining performance comparable to uncompressed models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG systems can struggle with noise in retrieved documents, making the inference process slow and expensive. To fix this, the paper introduces a new method called AdaComp. It helps RAG systems by adapting how much information is kept from the original text. The method works by looking at what’s needed to answer a question and then using that information to predict what’s important. This makes the system faster and more efficient without sacrificing its ability to give good answers. |
Keywords
» Artificial intelligence » Inference » Rag » Retrieval augmented generation
