Papers by Peidong Wang
TIGER: A Unified Generative Model Framework for Multimodal Dialogue Response Generation (2024.lrec-main)
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| Challenge: | Existing research on multimodal dialogues focuses on textual response generation and visual response selection based on the dialogue context. |
| Approach: | They propose a generative model framework for multimodal dialogue response generation that ground the conversation on an image. |
| Outcome: | The proposed system provides users with an enhanced conversational experience. |
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch (2024.acl-long)
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Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song
| Challenge: | Prior studies on stickers focused on sentiment analysis and recommendation systems, overlooking their vast potential in empathetic response generation. |
| Approach: | They propose a multimodal empathetic dialogue dataset, STICKERCONV, which simulates human behavior with stickers, and propose evaluative metrics based on LLM. |
| Outcome: | The proposed framework generates contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging e-dialog systems. |
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025.findings-acl)
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Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang
| Challenge: | Existing models of seeker simulations are limited by the cost and ethical concerns of involving real seekers in mental health research. |
| Approach: | They propose an emotional and cognitive dynamic agent system equipped with tertiary memory to enable dynamic control of the simulator's configurations. |
| Outcome: | The proposed system achieves more realistic seeker simulation compared to baselines. |
DR-HM: Distill-then-Reinforce Training with Cognition-Aware Data Synthesis for Harmful Meme Detection (2026.findings-acl)
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Zihan Cheng, Jianxiang Ma, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Daling Wang, Yifei Zhang, Mingfu Zhang
| Challenge: | Current methods for harmful meme detection lack the knowledge required to identify such hate . current methods lack the ability to identify cultural stereotypes and visual metaphors . |
| Approach: | They propose a framework that decomposes meme analysis into a human-inspired reasoning process . they propose DR-HM to transfer knowledge from closed-source models while mitigating biases . |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation (2025.findings-acl)
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Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit, Peidong Wang, Jian Xue, Dinesh Manocha, Jinyu Li
| Challenge: | Generative Error Correction (GEC) is a powerful post-processing method to boost the performance of Automatic Speech Recognition systems. |
| Approach: | They propose a method to augment GEC models with retrieved entities to improve accuracy in out-of-domain and out-od scenarios. |
| Outcome: | The proposed method outperforms baseline models on multiple datasets and settings. |
Language Models as Continuous Self-Evolving Data Engineers (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their further evolution is often hampered by the scarcity of high-quality training data and the heavy reliance of traditional methods on expert-labeled data. |
| Approach: | They propose a paradigm that enables LLMs to train themselves by generating, cleaning, reviewing and annotating data with preference information. |
| Outcome: | The proposed model can generate, clean, review, and annotate data with preference information significantly reducing time and cost of post-training data construction. |
NEAT: Neuron-Based Early Exit for Large Reasoning Models (2026.findings-acl)
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| Challenge: | Existing approaches to reduce overthinking require additional rollout computation or externally labeled datasets. |
| Approach: | They propose a Neuron-based Early reAsoning exiT framework that monitors neuron-level activation dynamics to enable training-free early exits. |
| Outcome: | The proposed framework reduces the amount of reasoning steps generated by LRMs while maintaining accuracy. |
SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)
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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang
| Challenge: | Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors . |
| Approach: | They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details. |
| Outcome: | The proposed method improves accuracy, inference efficiency, and real-time processing capabilities. |