Papers by Wenda Liu
ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)
Copied to clipboard
Wenda Liu, Song Zhigang, Shuai Nie, Guangyao Liu, Lisung Chen, Binyu Yang, Yaran Chen, Peng Zhou, Hongzhen Wang, Yuchen Liu, Wenyue Hu, Jiaming Xu, Runyu Shi, Ying Huang
| Challenge: | ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines . |
| Approach: | They propose a Macro-to-Micro progressive learning approach that improves UIE without external information. |
| Outcome: | ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone. |
Eeyore: Realistic Depression Simulation via Expert-in-the-Loop Supervised and Preference Optimization (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been explored for mental healthcare training and therapy client simulation, but they fail to authentically capture diverse client traits and psychological conditions. |
| Approach: | They propose an 8B model optimized for realistic depression simulation with expert input at every stage. |
| Outcome: | The model outperforms GPT-4o in linguistic authenticity and profile adherence. |
TOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown remarkable advances in enabling language agents to tackle real-world tasks. |
| Approach: | They propose a tool-using agent-based CAD framework that automates text-to-CAD modeling . they propose an interactive CAD gym to roll out reasoning and tool-augmented interaction trajectories with the CAD engine . |
| Outcome: | The proposed framework can generalize across complex modeling tasks, supporting their open-source counterparts. |
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback (2024.findings-naacl)
Copied to clipboard
Wenda Xu, Daniel Deutsch, Mara Finkelstein, Juraj Juraska, Biao Zhang, Zhongtao Liu, William Yang Wang, Lei Li, Markus Freitag
| Challenge: | Recent large language models (LLMs) are leveraging human feedback to improve their output quality. however, human feedback is costly to collect, especially at inference time when the model provides new, unseen input. |
| Approach: | They propose an inference-time optimization method to refine large language models' output based on fine-grained feedback to pinpoint defects and guide iterative refinement . |
| Outcome: | The proposed method consistently outperforms baseline approaches on three text generation tasks, including machine translation, long-form question answering, and topical summarization. |