Multilingual Prompting for Automated Language Model Hallucination Detection
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Solution Overview
Problem
Existing language models generate hallucinations, which are difficult to detect manually or automatically, reducing their utility and requiring impractical manual re-training to correct.
Innovation Solution
Prompt a language model to generate responses in multiple languages, calculate semantic similarity between embeddings, and determine hallucinations based on similarity thresholds, allowing for automated detection and potential re-training or fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual detection and re-training is used to prevent hallucinations, then hallucination accuracy can be improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The system enables self-service by allowing the language model to automatically detect its own hallucinations through cross-lingual semantic similarity analysis, eliminating the need for manual verification and reducing the time required for detection and correction
Solution Approach 2:
The patent replaces manual mechanical detection processes with an automated computational system that uses embedding representations and semantic similarity calculations to detect hallucinations, significantly reducing time and human effort required
2Reliability
If manual verification is required for language model outputs, then reliability of content can be improved, but productivity and ease of operation decrease
Solution Approach 1:
The language model performs self-verification by generating responses in multiple languages and automatically assessing their semantic similarity, enabling it to ensure its own output reliability without requiring external manual verification, thus maintaining high productivity
3Reliability
If re-training language models to eliminate hallucinations is performed, then hallucination reduction can be achieved, but device complexity and loss of time increase
Solution Approach 1:
The system performs preliminary detection of hallucinations in real-time during operation using cross-lingual semantic similarity analysis, allowing for immediate identification and correction without requiring complex re-training procedures, thus reducing overall system complexity
Solution Approach 2:
Instead of changing the fundamental model structure through re-training, the patent changes the detection parameters by using embedding representations and semantic similarity thresholds, providing a simpler approach to hallucination reduction that avoids complex model modification
Data Source
AI summary
Aspects of the present disclosure relate to detecting hallucinations in language model outputs. Embodiments include receiving a user query. Embodiments further include prompting a language processing machine learning model to generate responses to the user query in each language of a set of multiple languages. Embodiments further include receiving the responses from the language processing machine learning model in response to the prompting. Embodiments further include creating embedding representations of the responses. Embodiments further include calculating, based on the embedding representations, a degree of semantic similarity between the responses. Embodiments further include determining that a response of the responses contains a model hallucination based on comparing the degree of semantic similarity between the responses to a threshold.