Multimodal RAG Response Evaluation for Hallucination Control
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Solution Overview
Problem
Large language models (LLMs) often produce hallucinations, particularly in multimodal retrieval augmented generation (RAG) systems, leading to inaccurate or irrelevant responses due to incorrect context selection and generation, which can perpetuate errors and contribute to model collapse, bias amplification, and data poisoning.
Innovation Solution
Implement a response evaluator that assesses the relevance and correctness of multimodal RAG outputs by partitioning responses into spans, calculating relevancy scores based on query similarity, and correctness scores against enterprise data, using neural networks to evaluate the accuracy of each span, thereby reducing hallucinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs generate responses without rigorous evaluation, then response generation speed is improved, but hallucination rate increases
Solution Approach 1:
The response evaluation process is segmented into multiple independent components: span partitioning divides the response into evaluable units, relevancy scoring evaluates each span separately against retrieved context, and correctness scoring verifies factual accuracy. This segmentation allows efficient parallel processing while maintaining comprehensive evaluation coverage, resolving the contradiction between speed and reliability.
Solution Approach 2:
The system performs preliminary evaluation actions by computing relevancy and correctness scores for each span before final response output. This preliminary scoring mechanism identifies potentially hallucinated content early in the processing pipeline, allowing the system to flag or correct issues before they propagate, thus maintaining both speed and reliability.
2Measurement precision
If response evaluation is performed with detailed span-level analysis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The complex evaluation task is segmented into manageable span-level units, where each span is independently evaluated for relevancy and correctness. This segmentation transforms a single complex evaluation problem into multiple simpler sub-problems that can be processed in parallel, achieving high measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces intermediary evaluation components (relevancy scorer, correctness scorer, span partitioner) that mediate between the generated response and final evaluation results. These intermediaries break down the complex evaluation process into discrete, modular functions that can be developed, tested, and maintained independently, reducing overall system complexity while maintaining high evaluation precision.
3Reliability
If multiple scoring mechanisms are implemented, then reliability is improved, but loss of energy increases
Solution Approach 1:
The system implements partial evaluation by focusing scoring efforts on critical spans identified through partitioning, rather than uniformly evaluating all response content with equal depth. This selective approach applies multiple scoring mechanisms where most needed while reducing computational energy consumption on less critical portions, balancing reliability improvement with energy efficiency.
Data Source
AI summary
Systems and methods for evaluating multimodal retrieval augmented generation (RAG) performance. The systems and methods include generating an internal response from a user input and a RAG database and generating a relevancy score for quantifying a relevance of the internal response to information retrieved from the RAG database based on the user input and a correctness score quantifying accuracy of the internal response to the information retrieved from the RAG database. The systems and methods further include generating a combined score from the relevancy score and correctness score and selectively performing a task based on the relevancy score, the correctness score, or the combined score.