Papers by Wenzhen Zheng
Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence, but training them from scratch is prohibitively expensive. |
| Approach: | They propose to continuously pre-train LLMs from existing pre-trained LLM models by using a set of parameters instead of randomly initializing them. |
| Outcome: | The proposed approach saves significant resources and accelerates convergence and performance. |
Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models (2025.emnlp-main)
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| Challenge: | Despite advances in large language models, their application to misinformation detection remains hindered by issues of logical inconsistency and superficial verification. |
| Approach: | They propose a multi-agent debate framework that reformulates misinformation detection as a structured adversarial debate based on fact-checking workflows . |
| Outcome: | The proposed framework enables iterative refinement of evidence while improving decision transparency. |
Scaling Laws for Code: A More Data-Hungry Regime (2026.acl-long)
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Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, Rongyi Zhang, Houyi Li, Siming Huang, YuanTao Fan, Wanxiang Che
| Challenge: | Code Large Language Models (LLMs) are revolutionizing software engineering, but scaling laws are primarily analyzed on Natural Language (NL). |
| Approach: | They fit Chinchilla law and Farsser law to test scaling laws for code . they find code is more data-hungry and requires higher data-to-parameter ratio . |
| Outcome: | The proposed scaling laws show that the more expressive Farsser law offers greater accuracy and scales with model size. |