Papers by Yongjian Chen
KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing LLMs lack systematic coverage of a bounded knowledge universe and compositional set-based reasoning over that universe. |
| Approach: | They propose a benchmark for multiple-choice questions based on 1,183 enumeration seeds . they use knowledge width, cardinality of required universe, reasoning depth to formalize the challenge . |
| Outcome: | The proposed benchmarks achieve only 5.26–36.88 F1 on universe enumeration and 16.00–44.19 accuracy on knowledge-grounded reasoning. |
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)
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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang
| Challenge: | Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting. |
| Approach: | They propose a representation-aware model merging framework for continual learning without access to historical data. |
| Outcome: | The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios. |
From Shortcuts to Balance: Attribution Analysis of Speech-Text Feature Utilization in Distinguishing Original from Machine-Translated Texts (2025.emnlp-main)
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| Challenge: | Recent work demonstrated strong performance in distinguishing machine-translated text from human-authored or human-transcribed content using pretrained language models. |
| Approach: | They find that bimodal integration reduces reliance on NEs while moderating overemphasis attribution patterns in speech features. |
| Outcome: | The proposed models show that they are more balanced while relying less on NEs. |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
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Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |