Papers by Shiping Yang
Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
Copied to clipboard
| Challenge: | Recent pre-trained transformer-based definition generation models lack effective representation learning to contain full semantic components of the given word, leading to under-specific definitions. |
| Approach: | They propose a novel contrastive learning method that encourages the model to capture more detailed semantic representations from the definition sequence encoding. |
| Outcome: | The proposed method could generate more specific definitions compared with state-of-the-art models. |
Multi-level Contrastive Learning for Script-based Character Understanding (2023.emnlp-main)
Copied to clipboard
| Challenge: | Scripts are written text for plays, movies, or broadcasts. |
| Approach: | They propose a multi-level contrastive learning framework to capture characters’ global information in a fine-grained manner. |
| Outcome: | The proposed framework improves on three character understanding sub-tasks by a considerable margin. |
Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data (2026.acl-long)
Copied to clipboard
Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu, Shining Liang, Ming Gong, Hongzhi Li, Hengyuan Zhang, Angel X. Chang, Dongmei Zhang
| Challenge: | Existing studies on robustness to explicit noise (e.g., document semantics) but overlook implicit noise (spurious features). |
| Approach: | They propose a framework to quantify the robustness of RAGs against spurious features by integrating a data synthesis pipeline and a taxonomy. |
| Outcome: | The proposed framework quantifies the robustness of RALMs against spurious features. |
Edit-Wise Preference Optimization for Grammatical Error Correction (2025.coling-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been successful in grammatical error correction (GEC) but their strengths have yet to be fully demonstrated in GEC . |
| Approach: | They propose a method to optimize grammatical errors by assigning higher reward weights to edit tokens during preference optimization. |
| Outcome: | The proposed method outperforms baselines on English and Chinese datasets and achieves state-of-the-art performance. |
A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are capable of working with humans in real-world scenarios, but they are prone to generate hallucinations and misinformation when deployed for mission-critical tasks. |
| Approach: | They propose a self-check approach to detect factual errors in a zero-resource fashion by using reverse validation to generate a hallucination detection benchmark. |
| Outcome: | The proposed method outperforms baseline methods while costing fewer tokens and less time. |
A New Dataset and Empirical Study for Sentence Simplification in Chinese (2023.acl-long)
Copied to clipboard
| Challenge: | Sentence simplification is a valuable technique that can benefit language learners and children. |
| Approach: | They propose a dataset for assessing sentence simplification in Chinese using manual simplifications from human annotators. |
| Outcome: | The proposed dataset shows that Chinese sentences are more accessible to children and nonnative readers than English sentences. |
Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026.acl-long)
Copied to clipboard
Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang, Ngai Wong
| Challenge: | Existing studies show that stronger models are not always optimal teachers, suggesting a mismatch between the teacher’s output and the student’s learning ability. |
| Approach: | They propose a method that routes each prompt to its optimal teacher via a query-level router that jointly considers the student models’ learnability and teacher models’ response quality. |
| Outcome: | The proposed method outperforms baselines on six benchmarks including instruct tuning and math reasoning settings. |