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A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
Not provided
RAGfine-tuningquery clarification
2610.28686
Builder Relevance
2h ago80%
Abstract
This paper presents a method for improving query clarification in retrieval-augmented generation systems by using a lightweight fine-tuned model.
Reality Card
Core Claim
The lightweight fine-tuned LoRA model achieves 96.6% classification accuracy and outperforms larger models in both classification and quality of clarification generation.
Method / Result
Achieved 96.6% classification accuracy.
Limitations
The dataset and model may require specific configurations for optimal performance, which could affect reproducibility.
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