Parameter Ensembling for Factual Abstractive Summarization
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Solution Overview
Problem
Neural abstractive summarization systems frequently hallucinate information due to errors in training data, and existing methods to address this issue are inefficient and costly.
Innovation Solution
A document summarization framework called Contrastive Parameter Ensembling (CaPE) that combines parameters from a base model, an expert model, and an anti-expert model, trained on clean and noisy data subsets, to reduce hallucinations while retaining knowledge and diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural abstractive summarization models are trained on available training data, then the models can generate summaries, but hallucinations occur at high frequency due to errors in training data
Solution Approach 1:
The training data is segmented into clean and noisy subsets. The model is trained separately on clean data to learn accurate summarization patterns, and then fine-tuned on noisy data with correction mechanisms. This segmentation allows the model to distinguish between reliable and unreliable training examples, reducing hallucination while maintaining productivity.
Solution Approach 2:
The system implements feedback mechanisms where the model's generated summaries are evaluated against the original document content. When hallucinations are detected, correction signals are fed back to adjust the model's parameters. This feedback loop continuously improves factual accuracy while preserving the model's ability to generate summaries efficiently.
2Reliability
If training data is cleaned to remove errors, then hallucination frequency decreases, but the process is time-inefficient and costly
Solution Approach 1:
The system employs self-service mechanisms where the model automatically identifies and corrects errors in training data during the training process itself, without requiring external manual cleaning. The model uses its own generated summaries and attention mechanisms to detect inconsistencies and learn from corrected examples, significantly reducing the time and cost of data preparation while maintaining high factual accuracy.
3Productivity
If all training data is used including noisy samples, then model training is efficient, but hallucination frequency increases
Solution Approach 1:
The system dynamically changes training parameters such as learning rate, temperature, and attention weights based on the quality of training data being processed. When training on noisy data, the model adjusts parameters to reduce the influence of unreliable examples. This allows efficient use of all available training data while maintaining factual accuracy through adaptive parameter modification.
Data Source
AI summary
Embodiments described herein provide a document summarization framework that employs an ensemble of summarization models, each of which is a modified version of a base summarization model to control hallucination. For example, a base summarization model may first be trained on a full training data set. The trained base summarization model is then fine-tuned using a first filtered subset of the training data which contains noisy data, resulting in an “anti-expert” model. The parameters of the anti-expert model are subtracted from the parameters of the trained base model to produce a final summarization model which yields robust factual performance.