Summary of Retrieval-augmented Generation with Estimation Of Source Reliability, by Jeongyeon Hwang et al.
Retrieval-Augmented Generation with Estimation of Source Reliability
by Jeongyeon Hwang, Junyoung Park, Hyejin Park, Sangdon Park, Jungseul Ok
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Reliability-Aware Retrieval-Augmented Generation (RA-RAG) method addresses limitations in large language models by incorporating external databases, which often consult multiple sources. However, standard RAG methods overlook heterogeneous source reliability, making them prone to misinformation propagation. RA-RAG estimates source reliability and incorporates it into retrieval and aggregation processes, iteratively estimating true answers for a set of queries with no labelling. It selectively retrieves relevant documents from reliable sources using weighted majority voting, ensuring scalability without compromising performance. The effectiveness of RA-RAG is demonstrated on a benchmark designed to reflect real-world scenarios with heterogeneous source reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have limitations, such as hallucinations and outdated knowledge, which can be addressed by incorporating external databases through retrieval-augmented generation (RAG). However, current methods overlook the importance of reliable sources in these databases. A new method called Reliability-Aware RAG (RA-RAG) aims to improve this process by considering source reliability when retrieving and combining information. This approach is tested on a special benchmark that simulates real-world scenarios with different levels of reliability. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
