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PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction

2024-02-29 08:28:04
Erxin Yu, Jing Li, Chunpu Xu


Social media platforms are daily exhibiting millions of events. To preliminarily predict the mainstream public reaction to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous works focus on generating responses without factoring in popularity, we propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. Recognizing the noisy labels from user "likes", we tailor-make curriculum learning in proximal policy optimization (PPO) to help models capture the essential samples for easy-to-hard training. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models.

Abstract (translated)

社交媒体平台每天展示数百万个事件。为了初步预测这些事件对主流公众的反应,我们研究了针对社交媒体事件的流行回答预测,以自动生成受到大众欢迎的用户回复。与其他工作关注于生成没有考虑受欢迎程度的回答不同,我们提出了Popularity-Aligned Language Models (PopALM),通过强化学习区分受到更大受众喜欢的回答。为了识别用户“点赞”中的噪声标签,我们针对距离策略优化(PPO)进行了定制化的学习,以帮助模型捕捉易-难训练的关键样本。在实验中,我们为流行回答预测构建了一个庞大的微博数据集,结果表明PopALM可以提高高级语言模型的性能。



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