Using RAG to understand text similarity: When LLMs meet semantic search.
This paper proposes using RAG to enhance similarity search by leveraging generative models to capture nuanced semantic understanding between text pairs.
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https://arxiv.org/abs/2501.04006
Original Problem 🔍:
→ Traditional similarity search methods like string-based and vector-based approaches struggle to grasp nuanced semantic meanings, especially in specialized domains like biomedical text.
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Solution in this Paper 🛠️:
→ The paper introduces a RAG-based approach that builds a conversational chain to evaluate sentence pair similarities.
→ It optimizes prompt engineering with specific temperature settings and example counts to enhance accuracy.
→ The solution processes the BIOSSES dataset containing 100 biomedical sentence pairs through an iterative evaluation system.
→ Each iteration rebuilds the conversational chain with adapted user prompts for precise similarity scoring.
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Key Insights 💡:
→ Moderate temperature values (0.5) provide optimal balance for capturing semantic nuances
→ Including 20 examples in system prompts yields best performance
→ Iterative processing, while computationally intensive, ensures accurate similarity assessment
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Results 📊:
→ Achieved Pearson correlation score of 0.905 at temperature 0.5
→ Outperformed previous benchmark of 0.871 on BIOSSES dataset
→ Optimal performance with 20 training examples in prompt
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