Reranker Training Data Generation Using LLM Synthetic Queries
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Solution Overview
Problem
Generating a training dataset for a specialized reranker model in information retrieval systems requires significant manual human effort and resources, particularly in formulating domain-specific queries and annotating document rankings.
Innovation Solution
Utilizing large language models (LLMs) to automate the generation of synthetic queries and rank documents based on relevance, reducing the need for manual intervention by generating a training dataset that includes synthetic queries and their associated document rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human effort is used to formulate domain-specific queries and annotate document rankings, then the quality and accuracy of the training dataset can be ensured, but the time consumption and resource requirements increase significantly
Solution Approach 1:
The system uses the LLM to automatically generate synthetic queries and rank documents without requiring human annotators. The LLM processes documents and generates training data autonomously, eliminating the need for manual intervention while maintaining dataset quality through the model's inherent understanding of language and relevance.
Solution Approach 2:
The system creates synthetic queries by generating artificial search queries that mimic real user searches. These synthetic queries are then used to train the reranker model, providing a scalable alternative to manually collected queries while preserving the essential characteristics of real search behavior.
2Quantity of substance
If more documents are retrieved for ranking in each synthetic query, then the training dataset comprehensiveness improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary retrieval of candidate documents using a retriever model before the LLM ranks them. This pre-filtering step reduces the number of documents that need to be processed by the computationally expensive LLM, while still ensuring that relevant documents are captured for training the reranker model.
Solution Approach 2:
The retrieval and ranking process is divided into two stages: first, a retriever model fetches candidate documents using efficient algorithms like BM25; second, the LLM ranks these candidates. This segmentation allows the system to handle large numbers of documents by distributing the computational workload across different models with different resource requirements.
Data Source
AI summary
Systems and methods for generating a training dataset for a reranker model. The methods comprise, for each document of a set of documents, using a large language model (LLM) to generate one or more synthetic queries related to the document; for each generated synthetic query, using the LLM to rank a plurality of documents of the set of documents associated with the synthetic query based on a relevance of the plurality of documents to the synthetic query; and generating the training dataset to include each synthetic query and the ranking of the plurality of documents associated with that synthetic query.