Intrinsic Evaluation of RAG Systems for Deep-Logic Questions

Junyi Hu; You Zhou; Jie Wang

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arXiv · 2410.02932

Intrinsic Evaluation of RAG Systems for Deep-Logic Questions

Junyi Hu·You Zhou·Jie Wang

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Abstract

We introduce the Overall Performance Index (OPI), an intrinsic metric to evaluate retrieval-augmented generation (RAG) mechanisms for applications involving deep-logic queries. OPI is computed as the harmonic mean of two key metrics: the Logical-Relation Correctness Ratio and the average of BERT embedding similarity scores between ground-truth and generated answers. We apply OPI to assess the performance of LangChain, a popular RAG tool, using a logical relations classifier fine-tuned from GPT-4o on the RAG-Dataset-12000 from Hugging Face. Our findings show a strong correlation between BERT embedding similarity scores and extrinsic evaluation scores. Among the commonly used retrievers, the cosine similarity retriever using BERT-based embeddings outperforms others, while the Euclidean distance-based retriever exhibits the weakest performance. Furthermore, we demonstrate that combining multiple retrievers, either algorithmically or by merging retrieved sentences, yields superior performance compared to using any single retriever alone.

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Junyi Hu, You Zhou, Jie Wang. 2024-10-03. Intrinsic Evaluation of RAG Systems for Deep-Logic Questions. https://arxiv.org/abs/2410.02932

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