C a PE : Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization (2023)

Abstract: Hallucination is a known issue for neural abstractive summarization models. Recent work suggests that the degree of hallucination may depend on factual errors in the training data. In this work, we propose a new method called Contrastive Parameter Ensembling (CaPE) to use training data more effectively, utilizing variations in noise in training samples to reduce hallucination. Starting with a base model fine-tuned on an entire dataset, we additionally train expert and anti-expert models on clean and noisy subsets of the data, respectively. We then adjust the parameters of the base model by adding (subtracting) the parameters of the expert (anti-expert), advancing the recent work on additive parameter ensembling approaches. Trained on a much smaller data subset, expert and anti-expert models only fractionally (<14%) increases the total training time. Further, CaPE uses parameter ensembling and does not increase the inference time. Experimental results show that CaPE improves performance across different automatic factual metrics and human evaluation, with a maximum improvement of 16.69% and 15.38% on summary-level dependency-arc entailment accuracy for the XSUM and CNN/DM datasets. The CaPE model performs comparably to the base model on metrics of informativeness such as ROUGE.

## introduction neural abstractive summarization systems have been shown to generate plausible summaries with high lexical overlap with the references. however, human analyses @xcite @xcite and automatic evaluations @xcite @xcite show that state-of-the-art models trained on the widely used xsum @xcite and cnn/dm @xcite information with high frequency. the degree of a model's hallucinations further correlates with the quality of the training data @xcite . for instance, models trained on the xsum data tend to generate a higher proportion of factual errors as compared to models trained on the cnn/dm dataset. given the association between training data quality and hallucinations in resulting models, the easiest method to reduce hallucinations is to remove noisy samples from the training data @xcite . however, data filtering reduces the size of training data and consequently, the diversity in target summary since the removed noisy samples might also include useful task-specific knowledge. this impacts other aspects of the generated summaries such as information recall or fluency. in in this work, we design a simple yet effective strategy to utilize both clean and noisy training samples. based on the observation that the level of hallucination in a summarization model correlates with the level of noise in the training data, we propose contrastive parameter ensembling (cape), which adjusts the parameters of a base model by adding (subtracting) the weights of a model trained on clean (noisy) subsets of the data. this approach is motivated by recent work demonstrating the effectiveness of a simpler form of checkpoint averaging in neural machine translation @xcite . cape also builds on other recent work on model ensembling. @xcite ; @xcite combines expert and anti-expert predictions by taking a weighted average of their output. however, this requires running each model separately and increases computational cost linearly in the number of models, further slowing the auto-regressive generation of summaries. alternatively, @xcite proposed attention over parameters that jointly optimizes multiple models and directly combines all their parameters through learned attention coefficients. furthermore, @xcite recently showed that the average of a pretrained clip @xcite model with its another version that is further fine-tuned on a new data distribution performs better than both models on their complementary distributions. we evaluate our cape model on two benchmark abstractive summarization datasets, xsum and cnn/dm. we train an expert and an anti-expert corresponding to each of the dependency-arc entailment @xcite and entity overlap @xcite metrics. then, we combine each expert and anti-expert pair to obtain four variants of cape and evaluate them using the metrics used for data selection e-p src , as well as a different entailment metric, mnli @xcite , and two question answering-based metrics, questeval @xcite and qafacteval @xcite , for factual consistency. we find that all variants of our cape consistently outperform the state-of-the-art models on all factual metrics, with marginal variations in rouge scores and information recall. ## contrastive parameter ensembling in this work, we propose contrastive parameter ensembling (cape) for reducing hallucinations in text summarization systems. this method refines a base summarization model by training two additional models, an expert and an anti-expert model. an ensemble model is then constructed through a simple linear combination of the parameters of the three models, an approach inspired by recent work on weight (a.k.a. parameter)-space ensembling @xcite @xcite . ## results we evaluate cape on the xsum @xcite and cnn/dm @xcite datasets. the xsum data is highly abstractive and noisy, while cnn/dm is more extractive and contains fewer factual errors @xcite . these data variations allow us to evaluate cape under different data quality settings. besides the standard rouge-1/2/l (r1/r2/rl) scores, we use a diverse set of metrics for evaluating factual consistency and summary quality. the score of a summary sentence is the maximum entailment score over all input sentences, and the final score is averaged across summary sentences as in @xcite . ## related work abstractive text summarization metrics such as rouge @xcite and bertscore @xcite evaluate lexical and semantic overlap respectively but fail to sufficiently evaluate factuality and faithfulness @xcite . this has led to a line of research dedicated to evaluating factual consistency and hallucination in text summarization using new metrics such as entailment and question answering-based evaluation @xcite @xcite @xcite @xcite @xcite . the research focused on comparing these factual consistency evaluation metrics @xcite @xcite @xcite , however, often have contradicting observations. for instance, durmus et al. (2020) found that entailment-based automated metrics have lower correlations with factual consistency while pagnoni et al. (2021) concluded that the entailment-based factcc exhibits the highest correlations with human judgments of factual consistency. given the variations in findings from different human analyses of popular factual consistency evaluation metrics, we select a few metrics from each of the entailment, entity overlap, and qa-based evaluations, as well as use rouge and bertscore metrics for evaluating cape. along with the growing body of work on the analysis and evaluation of factual consistency, there has been some recent work on developing methods to enforce factual consistency in pre-trained language models. these include sampling techniques such as constrained decoding @xcite and neurologic decoding @xcite . another strategy is to control generation either by using language models to guide a base language model as in gedi @xcite and dexperts @xcite or via a hallucination knob @xcite . although these methods claim to be generic, they have not been successfully applied to constrain summary generation on the source document. comparatively, there are fewer papers that propose methods for factual consistency in text summarization. most of these focus on posthoc correction such as spanfact @xcite , contrast entity generation and selection @xcite , loss truncation @xcite , and encoding srl structure @xcite . @xcite uses focus attention and sampling to improve the diversity and faithfulness of summaries while @xcite uses data augmentation with a contrastive loss for factual consistency of abstractive summarization applied to customer feedback. finally, works focusing on data noise include revising hallucinated summaries in training data @xcite , dropping hallucinated samples (e.g. @xcite and @xcite for summarization, @xcite for headline generation), or defining curriculum based on the factual quality of training samples @xcite . ## conclusion we present contrastive parameter ensembling (cape) to reduce content hallucinations in abstractive summarization models. we first select clean (noisy) training samples to fine-tune an expert (antiexpert) model. then, we use the difference between the parameters of expert and anti-expert models to adjust the parameters of a base summarization model. we evaluate cape on the xsum and cnn/dm datasets using a diverse set of factual metrics, finding that cape effectively reduces hallucinations without a significant drop in rouge and information recall.