Papers by Duo 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. |
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser (2021.findings-emnlp)
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| Challenge: | Existing methods to build a visual dialog (VD) Questioner do not provide explicit guidance for questioner to generate visually related and informative questions. |
| Approach: | They propose a Related entity enhanced Questioner that learns entity-based questioning strategy from human dialogs. |
| Outcome: | The proposed approach achieves state-of-the-art performance on image-guessing task and question diversity. |
Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)
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| Challenge: | Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions. |
| Approach: | They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in the zero-shot directions. |
Towards Unifying Reference Expression Generation and Comprehension (2022.emnlp-main)
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| Challenge: | Existing models for REG and REC have distinct inputs and connections between them . a new model for REg and reprehension is needed to solve these problems . |
| Approach: | They propose a unified model for REG and REC that fuses image, region and text . they propose Vision-conditioned Masked Language Modeling and Text-Conditioned Region Prediction . |
| Outcome: | The proposed model outperforms existing models on REG and REC tasks. |
A Survey on Cross-Lingual Summarization (2022.tacl-1)
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| Challenge: | Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language. |
| Approach: | They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other . |
| Outcome: | The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis. |
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)
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| Challenge: | Existing approaches to building cross-lingual summarization systems on dialogue documents are limited. |
| Approach: | They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents. |
| Outcome: | The proposed model outperforms pipeline models on ClidSum and mDialBART. |
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. |