NLI Scoring for Hallucination Mitigation in Generative Transformers
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Solution Overview
Problem
Natural language generation models often produce hallucinations, generating text based on untrue facts, which can lead to inaccurate outputs in applications like news summarization and medical data, raising safety concerns and reducing trust in AI systems.
Innovation Solution
Implementing a natural language inference (NLI) scoring system that ranks tokens based on their faithfulness to the input content, using techniques like beam search and greedy search to select tokens that are true, false, or neutral, thereby mitigating hallucinations in generated text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If natural language generation models are used to generate text, then productivity and versatility are improved, but hallucinations occur reducing reliability
Solution Approach 1:
The system implements a feedback mechanism by generating multiple candidate tokens, evaluating their NLI scores to assess faithfulness to input content, and using this evaluation to select the most appropriate token. This closed-loop feedback process continuously monitors and corrects for hallucinations while maintaining generation productivity.
Solution Approach 2:
The system changes the parameter of token selection by incorporating NLI scores as an additional evaluation criterion beyond standard probability-based methods. This parameter change allows the model to balance between generation fluency and factual faithfulness, reducing hallucinations while maintaining productivity.
2Reliability
If NLI scoring system is implemented to reduce hallucinations, then reliability is improved, but device complexity increases
Solution Approach 1:
The system segments the token selection process into distinct stages: generating candidate tokens, evaluating each candidate's NLI score, and selecting the final token based on combined criteria. This segmentation makes the complex reliability improvement manageable by breaking it into modular, independently optimizable components.
Solution Approach 2:
The system applies partial action by evaluating only the top candidate tokens rather than all possible tokens, and by using approximate NLI scoring methods. This approach achieves sufficient reliability improvement without the full computational overhead of exhaustive evaluation, balancing complexity and performance.
3Measurement precision
If multiple ranking systems are used to evaluate tokens, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs partial evaluation by computing NLI scores only for the top-k candidate tokens rather than all possible tokens. This selective evaluation maintains measurement precision for the most promising candidates while significantly reducing the time loss associated with exhaustive evaluation of the entire token space.
Solution Approach 2:
The system performs preliminary filtering of candidate tokens based on basic probability criteria before applying the more computationally intensive NLI scoring. This preliminary action reduces the number of tokens requiring detailed evaluation, thereby maintaining measurement precision while minimizing time loss.
Data Source
AI summary
Systems and techniques are provided for natural language processing. A system generates a plurality of tokens (e.g., words or portions thereof) based on input content (e.g., text and/or speech). The system searches through the plurality of tokens to generate a first ranking the plurality of tokens based on probability. The system generates natural language inference (NLI) scores for the plurality of tokens to generate a second ranking of the plurality of tokens based on faithfulness to the input content (e.g., whether the tokens produce statements that are true based on the input content). The system generates output text that includes at least one token selected from the plurality of tokens based on the first ranking and the second ranking.