Bi-Encoder and Cross-Encoder Retrieval Fine-Tuning for Domain Accuracy
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Solution Overview
Problem
Large language models struggle to provide domain-specific and up-to-date information due to their general-purpose training, limiting their effectiveness in specialized domains like manufacturing, finance, and healthcare, and the quality of retrieved chunks significantly affects the performance of retrieve-then-read pipelines.
Innovation Solution
Fine-tuning bi-encoding and cross-encoding models using synthetic training data generated by large language models to identify and rank relevant chunks of information, enhancing the retriever model's ability to provide high-quality inputs to generative models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are trained on general-purpose data, then they can handle diverse tasks, but they cannot provide domain-specific and up-to-date information
Solution Approach 1:
The system segments the retrieval process into two distinct models: bi-encoding for initial retrieval and cross-encoding for re-ranking. This segmentation allows each model to be optimized for its specific function, with the cross-encoding model specializing in domain-specific accuracy while the bi-encoding model handles diverse query types efficiently.
Solution Approach 2:
The system performs preliminary retrieval using bi-encoding to identify candidate chunks before applying the more computationally intensive cross-encoding re-ranking. This preliminary action filters the search space, allowing the cross-encoding model to focus computational resources on refining the most promising candidates for domain-specific accuracy.
2Measurement precision
If cross-encoding model is used for re-ranking, then retrieval accuracy improves, but computational cost increases
Solution Approach 1:
The system applies cross-encoding re-ranking only to the top-k candidate chunks identified by bi-encoding, rather than re-ranking all retrieved chunks. This partial action maintains high retrieval accuracy for the most important results while significantly reducing the computational cost compared to applying cross-encoding to the entire corpus.
Solution Approach 2:
The bi-encoding model serves as an intermediary that pre-filters the corpus to identify candidate chunks worthy of cross-encoding re-ranking. This intermediary step bridges the gap between efficient initial retrieval and accurate final ranking, reducing the overall computational burden while maintaining high retrieval accuracy.
3Reliability
If more training data is used to fine-tune retriever models, then domain-specific performance improves, but data generation time increases
Solution Approach 1:
The system generates synthetic training data in advance using domain-specific guidelines and expert knowledge, storing it for future use in fine-tuning the cross-encoding model. This preliminary data generation eliminates the need for time-consuming manual annotation during deployment while ensuring high domain-specific performance through carefully crafted training examples.
Solution Approach 2:
The system uses automatically generated synthetic data with simulated relevance labels to fine-tune the retriever models, eliminating the need for manual expert annotation. This self-service approach to data generation significantly reduces the time and resources required while maintaining domain-specific performance through algorithmic generation of realistic training scenarios.
Data Source
AI summary
A method includes obtaining a raw text corpus and a set of seed queries. The method also includes training a retrieval pipeline having a bi-encoding model and a cross-encoding model using the raw text corpus and the set of seed queries. The method further includes outputting a trained bi-encoding model and a trained cross-encoding model. Training the retrieval pipeline includes (i) training the bi-encoding model to perform initial embedding-based retrieval of segments from the raw text corpus and (ii) training the cross-encoding model to perform re-ranking or refinement of the retrieved segments.