Papers by Ge Ren
Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)
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Ziqi Zhao, Zhaochun Ren, Jiahong Zou, Liu Yang, Zhiwei Xu, Xuri Ge, Zhumin Chen, Xinyu Ma, Daiting Shi, Shuaiqiang Wang, Dawei Yin, Xin Xin
| Challenge: | Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning. |
| Approach: | They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains. |
| Outcome: | Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE. |
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026.findings-acl)
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| Challenge: | Current approaches for Multimodal Sentiment Analysis (MSA) rely on parameter-heavy LLMs for classification, overlooking multimodal sentiment reasoning generation in resource-limited environments. |
| Approach: | They propose a multimodal sentiment reasoning distillation model that employs a teacher-assistant-student paradigm to address deployment constraints in resource-limited environments. |
| Outcome: | The proposed model performs well on a resource-limited JMSRC task with only 3B parameters and shows generalization and interpretability. |
UOR: Universal Backdoor Attacks on Pre-trained Language Models (2024.findings-acl)
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| Challenge: | Existing methods to attack pre-trained language models rely on manual selection of triggers and backdoor representations. |
| Approach: | They propose a backdoor attack method that turns manual selection into automatic optimization . they propose to use poisoned contrastive learning to learn more uniform backdoor representations . |
| Outcome: | The proposed method achieves better attack performance on text classification tasks compared to manual methods. |
Backdoor NLP Models via AI-Generated Text (2024.lrec-main)
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| Challenge: | Existing attacks disregard fluency and semantic fidelity of poisoned text, rendering it easily detectable. |
| Approach: | They propose to use AI-generated poisoned text to attack NLP models by establishing covert associations between trigger patterns and target labels without affecting normal accuracy. |
| Outcome: | The proposed method achieves effective attacks while maintaining fluency and semantic similarity across all scenarios. |
Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (2022.coling-1)
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| Challenge: | Sentiment analysis in social media is challenging because of the lack of context. |
| Approach: | They propose to use stickers to perform a multimodal sentiment analysis task using Chinese stickers. |
| Outcome: | The proposed model performs best compared with other models. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)
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Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju
| Challenge: | Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study. |
| Approach: | They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies . |
| Outcome: | The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%. |
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning (2021.findings-acl)
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| Challenge: | Existing methods for video captioning consider a sequence of frames and biases towards focused objects. |
| Approach: | They propose an Object-Oriented Non-Autoregressive approach to video captioning . it performs three steps: 1) identify the focused objects and predict their locations . 2) generate related attribute words and relation words of these focused objects to form a draft caption . |
| Outcome: | The proposed method achieves competitive results with the state-of-the-art methods but with higher diversity and faster inference speed. |
Hate Speech Detection Based on Sentiment Knowledge Sharing (2021.acl-long)
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| Challenge: | Existing methods for hate speech detection are stereotyped and biased . et al., a paper examining the effectiveness of multitask learning in hate speech recognition tasks . |
| Approach: | They propose a hate speech detection framework based on sentiment knowledge sharing . they extract affective features of the target sentence and use sentiment features from external resources . |
| Outcome: | The proposed model can detect hate speech over two public datasets. |