Summary of Lrp4rag: Detecting Hallucinations in Retrieval-augmented Generation Via Layer-wise Relevance Propagation, by Haichuan Hu et al.
LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation
by Haichuan Hu, Yuhan Sun, Quanjun Zhang
First submitted to arxiv on: 28 Aug 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 This research proposes a novel method called LRP4RAG to detect hallucinations in Retrieval-Augmented Generation (RAG) models. RAG is widely used to mitigate hallucinations in large language models, but current approaches still suffer from incomplete knowledge extraction and insufficient understanding, leading to persistent hallucinations. The proposed method utilizes Layer-wise Relevance Propagation (LRP) to compute the relevance between input and output of the RAG generator, followed by further processing and classification to determine whether the output contains hallucinations. Experimental results show that LRP4RAG outperforms existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is trying to fix a problem with big language models called hallucinations. Hallucinations happen when the model gives an answer that’s not really supported by the information it was given. To solve this, scientists are using something called Retrieval-Augmented Generation (RAG). But even with RAG, the models can still make mistakes. The new method, LRP4RAG, uses a special algorithm to figure out when the model is making an incorrect answer. |
Keywords
» Artificial intelligence » Classification » Rag » Retrieval augmented generation
