RAG Retriever Ranking Sequence for Lower Hallucination Outputs
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Solution Overview
Problem
Large language models tend to generate hallucinations and struggle to use external knowledge effectively, leading to challenges in real-world applications, and existing methods for optimizing retrieval-augmented generation (RAG) systems are limited in incorporating human feedback.
Innovation Solution
Implement a retriever model with a reconfigurable sequence of rankers, including bi-encoders, cross-encoders, and LLM-rankers, to identify relevant information chunks, and use a self-rewarding optimization technique to train the RAG architecture based on generated responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single ranker is used in the retriever model, then the system complexity is low, but the quality of retrieved information chunks is insufficient
Solution Approach 1:
The retriever model is segmented into multiple specialized rankers (bi-encoder ranker, cross-encoder ranker, and LLM-ranker), each designed to handle specific aspects of information retrieval. This segmentation allows each component to excel at its specific function while collectively providing comprehensive retrieval capability, resolving the contradiction between retrieval quality and system complexity.
Solution Approach 2:
Multiple rankers with different strengths are merged into a unified retriever model that processes queries through all rankers in sequence. The bi-encoder provides fast initial filtering, the cross-encoder refines results with pairwise interactions, and the LLM-ranker adds semantic understanding. This merging combines the advantages of each approach to achieve high retrieval quality without sacrificing too much complexity.
2Adaptability or versatility
If traditional RAG training methods are used, then training speed is fast, but the ability to incorporate human feedback is limited
Solution Approach 1:
The training methodology implements a feedback mechanism where human evaluations of generated responses are systematically incorporated to update the RAG system. Reward signals derived from human feedback guide the optimization process, allowing the system to adapt and improve based on actual performance metrics. This feedback loop enables continuous improvement while maintaining reasonable training efficiency through targeted updates rather than complete retraining.
Data Source
AI summary
A method includes obtaining training data for a retrieval-augmented generation (RAG) architecture having retriever and generative models. The retriever model is configured to identify information chunks relevant to input queries, and the generative model is configured to generate outputs based on the information chunks and the input queries. The method also includes generating a prompt for the generative model and generating multiple sets of queries for the retriever model. Each query in the multiple sets of queries is configured to cause the retriever model to select a set of information chunks associated with the prompt. The method further includes generating multiple responses to the prompt using the generative model and the sets of information chunks and determining rewards associated with the RAG architecture based on the responses. In addition, the method includes training the generative model based on the training data and the rewards to produce an updated RAG architecture.