Papers by Liwei Zheng
C2LEVA: Toward Comprehensive and Contamination-Free Language Model Evaluation (2025.findings-acl)
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Yanyang Li, Wong Tin Long, Cheung To Hung, Jianqiao Zhao, Duo Zheng, Liu Ka Wai, Michael R. Lyu, Liwei Wang
| Challenge: | Recent advances in large language models (LLMs) have shown significant promise, yet their evaluation raises concerns regarding data contamination due to the lack of access to proprietary training data. |
| Approach: | They propose a bilingual benchmark that offers a holistic evaluation and systematic contamination prevention. |
| Outcome: | The proposed evaluations of 15 open-source and proprietary models show that they are reliable and free of data contamination. |
NeuRAG: End-to-End Neural Knowledge Augmentation via Hyper-Neurons (2026.findings-acl)
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| Challenge: | Existing approaches to grounding large language models in external knowledge are constrained by a decoupled architecture: retrieval and reasoning operate as separate stages, with retrieved text merely prepended as passive context. |
| Approach: | They propose an end-to-end Neuralized RAG framework that unifies knowledge retrieval and fusion through Hyper-Neurons. |
| Outcome: | Extensive experiments across multiple datasets and LLMs demonstrate NeuRAG’s strong and consistent performance as a promising novel RAG paradigm. |
CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)
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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing. |
| Approach: | They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy. |
| Outcome: | CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding. |