PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction
<|reference_start|>PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction: 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.<|reference_end|>
@article{yu2024popalm:,
    title={PopALM: Popularity-Aligned Language Models for Social Media Trendy
  Response Prediction},
    author={Erxin Yu, Jing Li, Chunpu Xu},
    journal={arXiv preprint arXiv:2402.18950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18950},
    primaryClass={cs.CL}
}