Challenges with Large Language Models (LLMs)
Static Knowledge Base: LLMs often provide outdated information because their knowledge is fixed.
Inaccuracy and Fabrication: They can create incorrect or fabricated responses, leading to confusion.
Enhancing Accuracy with RAG
Retrieval-Augmented Generation (RAG): This method integrates real-time information to improve the relevance and accuracy of responses.
Query Rewriting: To retrieve better documents, we need to refine user queries that may be unclear or ambiguous.
Current Query Rewriting Methods
There are two main approaches:
- Training-Based: Uses supervised learning with labeled data.
- Prompt-Based: Employs prompt engineering to guide LLMs in rewriting queries.
Multi-Strategy Rewriting: Combines various prompt-based techniques to improve query handling and retrieval diversity.
Introducing DMQR-RAG
DMQR-RAG: Developed by researchers from several universities and Kuaishou Technology, this framework enhances query rewriting with four strategies:
- GQR: Cleans queries by removing irrelevant information.
- KWR: Extracts preferred keywords for better search results.
- PAR: Creates a pseudo answer to expand the query.
- CCE: Focuses on key information from detailed queries.
Adaptive Strategy Selection
This method selects the best rewriting strategies for each query, reducing unnecessary rewrites and improving retrieval performance.
Proven Results
Through extensive testing, DMQR-RAG has shown:
- 10% Performance Improvement: Outperforms baseline methods.
- Higher Recall and Precision: Achieved significant gains in datasets like FreshQA.
- Effective for Smaller LLMs: Reduces noise and enhances overall performance.
Conclusion
DMQR-RAG successfully enhances document retrieval systems, ensuring more relevant and diverse results. It has proven effective across 15 million user queries, increasing accuracy and relevance without compromising performance.
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