Summary of Multi-source Knowledge Pruning For Retrieval-augmented Generation: a Benchmark and Empirical Study, by Shuo Yu (1) et al.
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
by Shuo Yu, Mingyue Cheng, Jiqian Yang, Jie Ouyang, Yucong Luo, Chenyi Lei, Qi Liu, Enhong Chen
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 This paper proposes a novel approach to mitigate the hallucination of large language models (LLMs) by integrating external knowledge. The study focuses on retrieval-augmented generation (RAG), which combines structured and unstructured knowledge from diverse domains. To address the lack of suitable datasets, the authors standardize a benchmark dataset that incorporates multiple knowledge sources. Building upon this dataset, they identify the limitations of existing methods and develop PruningRAG, a plug-and-play RAG framework that uses multi-granularity pruning strategies to mitigate misleading information. Experimental results demonstrate superior performance of PruningRAG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem with large language models. These models can sometimes make up information that’s not true! The study shows how to use external knowledge to help prevent this from happening. It does this by combining different types of knowledge from many sources, which is like real life where we often learn things from multiple places. To test this idea, the researchers created a special dataset with lots of different kinds of information. They also developed a new way to make sure the model doesn’t get tricked into making up fake facts. Their results show that their method works better than other ways people have tried. |
Keywords
» Artificial intelligence » Hallucination » Pruning » Rag » Retrieval augmented generation
