Summary of Intrinsic Evaluation Of Rag Systems For Deep-logic Questions, by Junyi Hu et al.
Intrinsic Evaluation of RAG Systems for Deep-Logic Questions
by Junyi Hu, You Zhou, Jie Wang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 researchers introduce a new metric called Overall Performance Index (OPI) to evaluate retrieval-augmented generation (RAG) mechanisms for deep-logic queries. OPI is calculated as the harmonic mean of two key metrics: Logical-Relation Correctness Ratio and BERT embedding similarity scores between ground-truth and generated answers. The paper applies OPI to assess LangChain, a popular RAG tool, using a logical relations classifier fine-tuned from GPT-4o on the RAG-Dataset-12000 from Hugging Face. The results show a strong correlation between BERT embedding similarity scores and extrinsic evaluation scores. The study finds that combining multiple retrievers, either algorithmically or by merging retrieved sentences, yields superior performance compared to using any single retriever alone. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to measure how well computer programs can answer tricky questions. They call it the Overall Performance Index (OPI). It looks at two things: how correct the answers are and how similar they are to what the program is supposed to say. They tested this with a tool called LangChain, which helps programs generate text based on what they find online. The results show that using multiple ways to get information can help programs give better answers. |
Keywords
» Artificial intelligence » Bert » Embedding » Gpt » Rag » Retrieval augmented generation
