Information popularity prediction, aiming to predict the growth of user participation in a trending topic diffusion, is a fundamental task in social networks. Existing methods often treat information diffusion as a single independent process, ignoring the ``public opinion field effect'' where multiple trending topics coexist and compete for user attention simultaneously. Inspired by Hawkes theory, we propose a novel Hawkes-process-based learning model for information popularity prediction, which takes into account both the temporal correlation among users' propagation behaviors in several topics diffusion and public opinion field effect in social networks. We first propose an improved neural Hawkes process to capture comprehensive propagation law from multiple dimensions and then propose a novel public opinion field paradigm based on the improved Hawkes process and cascade structure. We design a novel learning framework incorporating the public opinion field paradigm to extract high-quality representations for information popularity prediction. Extensive experiments on four real-world datasets validate that our model significantly outperforms the state-of-the-art competitors.

In recent years, numerous tasks have been proposed to encourage model to develop specified capability in understanding audio-visual scene, primarily categorized into temporal localization, spatial localization, spatio-temporal reasoning, and pixel-level understanding. Instead, human possesses a unified understanding ability for diversified tasks. Therefore, designing an audio-visual model with general capability to unify these tasks is of great value. However, simply joint training for all tasks can lead to interference due to the heterogeneity of audiovisual data and complex relationship among tasks. We argue that this problem can be solved through explicit cooperation among tasks. To achieve this goal, we propose a unified learning method which achieves explicit inter-task cooperation from both the perspectives of data and model thoroughly. Specifically, considering the labels of existing datasets are simple words, we carefully refine these datasets and construct an Audio-Visual Unified Instruction-tuning dataset with Explicit reasoning process (AV-UIE), which clarifies the cooperative relationship among tasks. Subsequently, to facilitate concrete cooperation in learning stage, an interaction-aware LoRA structure with multiple LoRA heads is designed to learn different aspects of audiovisual data interaction. By unifying the explicit cooperation across the data and model aspect, our method not only surpasses existing unified audio-visual model on multiple tasks, but also outperforms most specialized models for certain tasks. Furthermore, we also visualize the process of explicit cooperation and surprisingly find that each LoRA head has certain audio-visual understanding ability. Code and dataset: https://github.com/GeWu-Lab/Crab

Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Direct Preference Optimization (DPO) is a widely used reinforcement learning from human feedback (RLHF) method across various domains.Recent research has increasingly focused on the role of token importance in improving DPO effectiveness.It is observed that identical or semantically similar content (defined as ambiguous content) frequently appears within the preference pairs.We hypothesize that the presence of ambiguous content during DPO training may introduce ambiguity, thereby limiting further improvements in alignment.Through mathematical analysis and proof-of-concept experiments, we reveal that ambiguous content may potentially introduce ambiguities, thereby degrading performance.To address this issue, we introduce Ambiguity Awareness Optimization (AAO), a simple yet effective approach that automatically reweights ambiguous content to reduce ambiguities by calculating semantic similarity from preference pairs.Through extensive experiments, we demonstrate that AAO consistently and significantly surpasses state-of-the-art approaches in performance, without markedly increasing response length, across multiple model scales and widely adopted benchmark datasets, including AlpacaEval 2, MT-Bench, and Arena-Hard.Specifically, AAO outperforms DPO by up to 8.9 points on AlpacaEval 2 and achieves an improvement of by up to 15.0 points on Arena-Hard.