Papers by Chaozheng Wang
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models have demonstrated remarkable capabilities in open-domain dialogues, but their performance in service dialogues remains suboptimal. |
| Approach: | They propose a framework that enables agents to learn effective strategies without large-scale human annotations. |
| Outcome: | The proposed framework decouples user modeling into two components that provide adaptive training scenarios rather than acting as an unfair adversary. |
Learning to Ask: When LLM Agents Meet Unclear Instruction (2025.emnlp-main)
Copied to clipboard
Wenxuan Wang, Shi Juluan, Zixuan Ling, Yuk-Kit Chan, Chaozheng Wang, Cheryl Lee, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, Michael R. Lyu
| Challenge: | Despite their impressive capabilities, LLMs struggle with complex computations and delivering accurate, timely information. |
| Approach: | They propose a framework that prompts LLM agents to ask questions when they encounter obstacles due to unclear instructions and an automated evaluation tool called ToolEvaluator. |
| Outcome: | The proposed framework outperforms existing frameworks for tool learning in the Noisy ToolBench. |
XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection (2024.findings-acl)
Copied to clipboard
| Challenge: | XMoE leverages small experts and a threshold-based router to selectively engage only essential parameters. |
| Approach: | They propose a novel MoE that leverages small experts to selectively engage only essential parameters. |
| Outcome: | The proposed model can reduce computation load at MoE layers by over 50% without sacrificing performance. |
Split and Merge: Aligning Position Biases in LLM-based Evaluators (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown promise as automated evaluators for assessing the quality of answers generated by AI systems. |
| Approach: | They propose an alignment-based system that calibrates position bias in a lightweight yet effective manner by taking into account both length and semantics and combining them into a single prompt. |
| Outcome: | Extensive experiments with six LLMs on 11,520 answer pairs show that PORTIA significantly improves consistency and consistency rates with humans. |