Papers by Chong Tian
PRIM: Towards Practical In-Image Multilingual Machine Translation (2025.emnlp-main)
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| Challenge: | Current research on in-image machine translation focuses on synthetic data with simple background, single font, fixed text position, and bilingual translation. |
| Approach: | They propose an end-to-end model to handle the challenge of practical conditions in PRIM . they annotate a real-world one-line text image with complex background, fonts, diverse text positions . |
| Outcome: | The proposed model improves translation quality and visual effect compared to other models. |
A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection (2025.emnlp-main)
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| Challenge: | Rapid LLM advancements heighten fake news risks by enabling the automatic generation of increasingly sophisticated misinformation. |
| Approach: | They propose a framework that implements an adversarial training paradigm by an agent symbolic learning optimization process rather than numerical updates. |
| Outcome: | The proposed framework generates sophisticated fake news that degrades state-of-the-art detection performance by 53.4% in Chinese and 34.2% in English on average. |
Enhancing Recommendation Explanations through User-Centric Refinement (2025.findings-emnlp)
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| Challenge: | Existing explanations for user reviews often fail to meet user-centric aspects, reducing their usefulness to users. |
| Approach: | They propose a paradigm that refines initial explanations generated by existing models during the inference stage to enhance their quality in multiple aspects. |
| Outcome: | The proposed model improves explanations generated by existing models during the inference stage to enhance their quality in multiple aspects. |
Shorten After You’re Right: Lazy Length Penalties for Reasoning RL (2026.findings-acl)
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Danlong Yuan, Tian Xie, Shaohan Huang, Huishuai Zhang, Zhuocheng Gong, Chong Luo, Furu Wei, Dongyan Zhao
| Challenge: | Existing shortening methods for long reasoning models rely on additional supervision or multi-stage post-training. |
| Approach: | They propose a lazy length penalty that imposes length pressure on models without extra training stages. |
| Outcome: | The proposed method significantly reduces response length without extra training stages while maintaining or improving performance. |
V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang
| Challenge: | Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia. |
| Approach: | They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS). |
| Outcome: | The proposed framework provides quantitative analyses and superior deciphering capability. |
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation (2023.emnlp-main)
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Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang
| Challenge: | Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration. |
| Approach: | They propose a framework that allows the model to "cheat" the objective by duplicating knowledge segments in a superficial pattern matching based on overlap. |
| Outcome: | The proposed framework can be applied to a WoW dataset and shows that it works across models and decoding strategies. |