Papers by Rong Ding
Token-Level Self-Evolution Training for Sequence-to-Sequence Learning (2023.acl-short)
Copied to clipboard
| Challenge: | Adaptive training approaches do not consider the variation of learning difficulty in different training steps, making the learning deterministic and sub-optimal. |
| Approach: | They propose a dynamic token-level self-evolution training method that reweighs the training losses of different target tokens based on priors. |
| Outcome: | Empirically, the proposed method yields significant improvements on three translation tasks. |
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
Copied to clipboard
Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
| Approach: | They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss. |
| Outcome: | Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment. |
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)
Copied to clipboard
Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun
| Challenge: | Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks. |
| Approach: | They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies. |
| Outcome: | The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%. |
Harnessing Large Language Models for Disaster Management: A Survey (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including their emerging role in mitigating threats to human life, infrastructure, and the environment during natural disasters. |
| Approach: | They propose a taxonomy that categorizes existing LLMs based on disaster phases and application scenarios to provide valuable insights for the research community and practitioners . |
| Outcome: | The proposed taxonomy categorizes existing LLMs based on disaster phases and application scenarios. |
CASN:Class-Aware Score Network for Textual Adversarial Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to counteract adversarial attacks can be divided into two directions, adversarials defense and adversarially detection. |
| Approach: | They propose a score-based generative method to implicitly model the data distribution using a log-density distribution and supervised contrastive learning to guide the estimation using label information. |
| Outcome: | The proposed method improves on three text classification tasks on four advanced attack algorithms. |
The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios (2026.findings-acl)
Copied to clipboard
Daocheng Fu, Jianbiao Mei, Rong Wu, Xuemeng Yang, Jia Xu, Ding Wang, Pinlong Cai, Yong Liu, Licheng Wen, Botian Shi
| Challenge: | Existing research mainly focuses on performance upper bounds in static environments, overlooking stochastic real-world deployment. |
| Approach: | They propose a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting. |
| Outcome: | The proposed model evaluates agents in a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting. |
HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)
Copied to clipboard
Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao
| Challenge: | Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences. |
| Approach: | They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning. |
| Outcome: | The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks. |