Abstract
As more than 70$\%$ of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are reluctant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the over-reliance on a specific framework is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, data augmentation based on large language models faces two disadvantages: 1) the potential issues or toxicity in the augmented data; 2) the expensive costs. Therefore, in this paper, we propose a novel data augmentation framework based on both large and small language models for debiasing opinion summarization. In specific, a small size of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on confusion degree and sentiment classification. Experiments have proved that our framework can effectively alleviate emotional bias same as using only large models, but more economically.
Abstract (translated)
由于现有观点总结数据集中的超过70%好评，现有的观点总结方法不愿意根据负面文本生成负面摘要。为了解决这种情感偏见，一种不依赖特定框架的直接方法是根据大型语言模型生成额外数据来平衡数据集的情感分布。然而，基于大型语言模型的数据增强存在两个缺点：1）增强数据的潜在问题或毒性；2）昂贵的成本。因此，在本文中，我们提出了一个基于大型和小型语言模型的观点总结去偏新方法。具体来说，通过大型语言模型重新编写积极文本可以获得小的负面评论数量。然后，基于生成的数据训练解离重构模型。训练后，可以通过解码不同样本表示的组合以及根据混淆程度和情感分类进行过滤来获得大量合成数据。实验证明，我们的框架可以有效地消除仅使用大型模型时存在的情感偏见，而且更加经济实惠。
URL
https://arxiv.org/abs/2403.07693
