Papers by Chunpu Xu
Understanding Social Media Cross-Modality Discourse in Linguistic Space (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on how images are structured with texts to form coherent meanings in human cognition have not addressed the problem. |
| Approach: | They propose a concept of cross-modality discourse which defines how human readers couple image and text understandings. |
| Outcome: | The proposed model shows that trendy encoders based on multi-head attention are unable to understand cross-modality discourse and modeling texts at the output layer helps yield the-state-of-the-art results. |
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
| Approach: | They propose a personalized topic attention model that captures salient contents to personalize hashtag contexts by predicting how likely a user will post with a hashtag. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. |
PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent work focuses on generic human responses without considering popularity factors in the social contexts. |
| Approach: | They propose Popularity-Aligned Language Models to distinguish responses liked by a larger audience through reinforcement learning. |
| Outcome: | The proposed model can distinguish responses liked by a larger audience through reinforcement learning. |
Topic-Guided Self-Introduction Generation for Social Media Users (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on social media use tags to profile users, but we have found that sentence-level self-introductions are more natural and engaging. |
| Approach: | They propose a novel topic-guided encoder-decoder framework that uses a user's tweeting history to generate a short sentence outlining their personal interests. |
| Outcome: | The proposed framework outperforms existing encoder-decoder models on a large-scale Twitter dataset and shows that it is more natural and engaging than previous approaches. |
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to image paragraph captioning ignore the past alignment information, resulting in repetitive captioning and incomplete captioning. |
| Approach: | They propose an Interactive key-value Memory-augmented Attention model for image paragraph captioning to keep track of attention history along with update-chain of decoder state. |
| Outcome: | Extensive experiments on a benchmark dataset demonstrate the effectiveness of the proposed model. |
Foresight Optimization for Strategic Reasoning in Large Language Models (2026.acl-long)
Copied to clipboard
Jessie Wang, Jiawen Duan, Jian Wang, Kaitao Song, Chunpu Xu, Johnny K. W. Ho, YU Fenggang, Johan F. Hoorn, Wenjie Li
| Challenge: | Existing reasoning enhancement methods do not capture foresight in LLMs. |
| Approach: | They propose to integrate opponent modeling principles into policy optimization to enhance strategic reasoning in LLMs by integrating opponent modeling into policy. |
| Outcome: | The proposed method outperforms existing reasoning-based LLMs in out-of-domain scenarios and shows that it significantly enhances strategic reasoning across LLM of varying sizes and origins. |
Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)
Copied to clipboard
Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo
| Challenge: | Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model. |
| Approach: | They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering. |
| Outcome: | The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies. |
RePALM: Popular Quote Tweet Generation via Auto-Response Augmentation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that the wording of tweets can significantly impact popularity, reflected by user replies, retweets, and likes. |
| Approach: | They propose a novel approach to generate popular quote tweets by leveraging augmented auto-responses from readers to align language generation with popularity. |
| Outcome: | The proposed model outperforms existing models that do not incorporate response augmentation and can generate popular quote tweets with augmented auto-responses. |
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)
Copied to clipboard
Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang
| Challenge: | Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences. |
| Approach: | They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. |
| Outcome: | The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner. |
Muffin: Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have shown that emotional support conversation models generate unhelpful responses that can hinder their effectiveness. |
| Approach: | They propose a model-agnostic framework called Mitigating unhelpfulness with multifaceted AI feedback for emot io nal support (Muffin) it uses a multifaceted feedback module to assess helpfulness model responses across various facets of emotional support and contrasts helpful and unhelpful responses generated by the model. |
| Outcome: | The proposed framework reduces the likelihood of unhelpful responses by comparing helpful and unhelpfully responses generated by previous models to improve response fluency and relevance. |
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification (2022.emnlp-main)
Copied to clipboard
| Challenge: | Social media users are using images and text to voice opinions and share ideas. |
| Approach: | They propose to use user comments to extract hinting features from user comments and explore them via self-training. |
| Outcome: | The proposed framework improves on four social media benchmarks for image-text relation classification, sarcasm detection, sentiment classification, and hate speech detection. |
Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States (2025.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks assess basic Theory of Mind abilities but neglect temporal evolution of mental states in real-world social contexts. |
| Approach: | They propose a benchmark specifically designed to evaluate Large Language Models' ability to understand and track the temporal progression of mental states across interconnected scenarios. |
| Outcome: | The proposed benchmarks underperform humans by 44.7% and show that they can model the dynamic nature of human mental states better than existing models. |