LLM Response Grounding With Claim-Evidence Alignment Scoring
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Solution Overview
Problem
Large Language Models (LLMs) in question-and-answer systems often generate hallucinations, leading to misleading or false information, and existing methods fail to effectively detect and mitigate these issues, compromising the reliability and trustworthiness of responses.
Innovation Solution
A system and method for automatically evaluating how well responses from LLMs are grounded in specific reference documents, using a combination of claims processor, evidence processor, alignment scorer, and metrics generators to identify and quantify hallucinations, providing fine-grained, interpretable metrics for response evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs are used to generate responses based on training data, then the system can provide relevant and human-like text, but hallucinations occur leading to misleading or false information
Solution Approach 1:
The patent segments the response evaluation into multiple independent components: claims extraction, evidence identification, alignment scoring, and metrics generation. Each component processes specific aspects of the response separately, allowing comprehensive factuality verification without compromising the overall response generation capability
Solution Approach 2:
The system implements feedback by generating alignment scores and grounding metrics that evaluate how well the LLM response aligns with the provided context. This feedback mechanism enables detection of hallucinations and provides actionable insights for improving response accuracy while maintaining generation flexibility
2Reliability
If knowledge base is connected to Q&A system to retrieve relevant facts, then response accuracy improves, but system complexity increases
Solution Approach 1:
The patent creates a universal evaluation framework that works across different Q&A system architectures and knowledge base types. The claims processor, evidence processor, and alignment scorer are designed to be architecture-agnostic, allowing the system to evaluate responses from various sources without requiring complex integration with specific knowledge base implementations
Solution Approach 2:
The patent introduces an intermediary evaluation layer that sits between the knowledge base retrieval and final response generation. This intermediary layer processes the relationship between retrieved facts and generated responses through alignment scoring, simplifying the overall system architecture by decoupling the knowledge base connection from the response evaluation
3Reliability
If LLM is given short pieces of information in prompt to answer questions, then hallucinations are reduced, but the system lacks metrics to detect remaining hallucinations
Solution Approach 1:
The patent replaces manual hallucination detection with an automated computational system. The alignment scorer uses machine learning models to automatically compare claims against evidence, and the metrics generator produces quantitative grounding scores, eliminating the need for manual fact-checking while maintaining high detection accuracy
Solution Approach 2:
The system enables self-service hallucination detection by generating automatic alignment scores and grounding metrics that evaluate the response's own consistency with the provided context. The system serves itself by identifying and measuring hallucinations without external intervention, providing actionable metrics that highlight specific ungrounded claims
Data Source
AI summary
Techniques for grounding automatically-generated responses produced by a question-and-answer system are provided. In one technique, a list of items and introductory text that is associated with the list of items are identified within text data. For each item in the list of items, a claim that is based on the introductory text and said each item is generated and the claim is added to a set of claims that is associated with the text data. For each claim in the set of claims, a score that reflects a level of support of said each claim in a set of documents is generated and the score is added to a set of scores for the set of claims. Data that is based on the set of scores is presented on a screen of a computing device.