LLM Response Grounding Through Critical Entity Matching
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Solution Overview
Problem
Large Language Models (LLMs) used in telecommunications and communications network services often produce hallucinations, which are factually incorrect or nonsensical inferences, posing challenges for applications requiring accurate and reliable information.
Innovation Solution
A grounding service that checks LLM responses against a knowledge base by extracting and comparing critical entities to ensure they match the knowledge base data, using Named Entity Recognition and relation extraction to mitigate hallucinations, and employs a counter mechanism to refine prompts when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM is used to generate responses in telecommunications and communications network services, then the functionality and capabilities are improved, but hallucinations occur causing factual inaccuracies
Solution Approach 1:
A grounding service is introduced as an intermediary component between the LLM and the end user. This service extracts entities from both the LLM response and the knowledge base, compares them to verify factual accuracy, and only allows the response to proceed if the entities match. This mediator resolves the contradiction by maintaining LLM functionality while ensuring reliability through automated verification.
2Reliability
If entity extraction and comparison is performed on all entities in the response, then factual accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
Instead of applying uniform entity verification to all entities in the response, the system identifies and verifies only critical entities that have the greatest impact on factual accuracy. This localized approach to verification maintains high grounding accuracy for the most important information while significantly reducing processing overhead compared to verifying every entity.
Solution Approach 2:
The verification process is segmented into distinct steps: extracting entities from the response, extracting entities from the knowledge base, comparing the two sets, and making a grounding decision. This segmentation allows the system to efficiently process only the necessary comparisons rather than performing exhaustive verification on all possible entity attributes.
3Reliability
If the grounding check fails, the system can re-submit the prompt to the LLM, but this increases processing time and number of API calls
Solution Approach 1:
The grounding service implements a feedback mechanism where the comparison result between response entities and knowledge base entities determines the next action. If entities match, the response is approved; if they don't match, the system can re-submit the prompt with feedback about the discrepancy. This structured feedback loop improves reliability while managing processing time through automated decision-making.
Data Source
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AI summary
Various examples relate to a method for grounding responses from a Large Language Model (LLM). The LLM is prompted using a prompt that has been augmented using information retrieved from a knowledge base and a response is received from the LLM. Entities are extracted from the response and from the information retrieved from the knowledge base. A grounding service checks the response is grounded with respect to the knowledge base by checking the types and values of critical entities extracted from the response match the types and values of critical entities extracted from the information retrieved from the knowledge base.