Language Model Response Correction via Context Verification
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Solution Overview
Problem
Conventional language models, particularly large language models, often generate incorrect or incomplete responses known as 'hallucinations' when processing context information, leading to inaccuracies and missing relevant details in their outputs.
Innovation Solution
A computer-implemented method that corrects responses generated by language models by identifying similar and supportive portions of context, using techniques such as embedding searches and textual entailment models to assess alignment with the context, and modifying responses to ensure accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional language models generate responses based on context information, then the models can perform natural language processing tasks, but the models sometimes generate incorrect or incomplete outputs known as hallucinations
Solution Approach 1:
The system implements a feedback mechanism where the generated response is evaluated against the original context by determining whether context portions support the response portions. This feedback loop identifies hallucinations and triggers correction processes, ensuring that the model learns from its errors and improves output reliability while maintaining versatility
Solution Approach 2:
The patent introduces an intermediary verification process between context input and final output. A separate evaluation step determines whether context portions support response portions, acting as a mediator that filters out hallucinations before they reach the final output, thereby improving reliability without sacrificing the model's natural language processing capabilities
2Adaptability or versatility
If language models are trained on large quantities of text data to improve task performance, then the models can perform a wide variety of natural language processing tasks, but the models may not fully understand domain-specific knowledge and terminology
Solution Approach 1:
The system applies local quality by performing targeted verification on domain-specific portions of the response. Instead of treating all content uniformly, the evaluation process specifically checks whether context portions support response portions containing domain knowledge and terminology, ensuring precise understanding in critical areas while maintaining overall task versatility
Solution Approach 2:
The patent implements preliminary action by evaluating context-response support relationships before finalizing the output. This pre-verification step ensures that domain-specific knowledge is accurately represented in the response before it is presented to the user, improving measurement precision in domain understanding while preserving the model's ability to handle various NLP tasks
Data Source
AI summary
One embodiment of a method for correcting a response generated by a machine learning model includes receiving the response from the machine learning model, where the response is generated by the machine learning model based on a request and a context, determining a plurality of portions of the context that are similar to one or more portions of the response, for each portion of the context included in the plurality of portions of the context, determining whether the portion of the context supports at least one portion of the response, and performing one or more operations to generate a corrected response based on the response and whether each portion of the context included in the plurality of portions of the context supports at least one portion of the response.