Papers by Weihua Du
DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026.acl-long)
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Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong
| Challenge: | Existing detectors are limited in their ability to detect large language models generated content in multilingual environments. |
| Approach: | They propose a multilingual benchmark to evaluate advanced detectors across 8 dimensions to better align with real-world applications. |
| Outcome: | The proposed benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse. |
Agentic-R1: Distilled Dual-Strategy Reasoning (2025.emnlp-main)
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| Challenge: | Current long chain-of-thought models rely on slow and error-prone natural language traces. |
| Approach: | They propose a framework that distills complementary reasoning strategies from multiple teachers into a unified student model. |
| Outcome: | The proposed framework improves accuracy on computation-intensive tasks and reduces inference latency on standard benchmarks. |
Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression (2026.acl-long)
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| Challenge: | Existing reinforcement learning pipelines suffer from degraded instruction following, excessive rollout costs, and strict context limits. |
| Approach: | They propose a reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use where context length quickly becomes a bottleneck. |
| Outcome: | The proposed framework improves the success rate while maintaining the same or even lower working context length compared to baselines. |
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables (2021.findings-emnlp)
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| Challenge: | Existing methods to achieve zero-shot translation suffer from spurious correlations between output language and language invariant semantics. |
| Approach: | They propose a method that denoizes the autoencoder objective based on pivot language into traditional training objective to improve translation accuracy on zero-shot directions. |
| Outcome: | The proposed method eliminates spurious correlations and outperforms state-of-the-art methods on two benchmark machine translation datasets. |
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step (2024.acl-long)
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Zehui Chen, Weihua Du, Wenwei Zhang, Kuikun Liu, Jiangning Liu, Miao Zheng, Jingming Zhuo, Songyang Zhang, Dahua Lin, Kai Chen, Feng Zhao
| Challenge: | Existing studies evaluate the tool utilization ability of large language models based on the final output or only consider the single-step tool calling. |
| Approach: | They propose a new approach to evaluate the tool utilization capability of large language models (LLMs) they decompose the tool usage into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. |
| Outcome: | The proposed model exhibits consistency with the outcome-oriented evaluation and provides a more fine-grained analysis of the capabilities of LLMs. |