Papers by Wenda Wang
ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)
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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. |
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
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| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |
CausalDialogue: Modeling Utterance-level Causality in Conversations (2023.findings-acl)
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| Challenge: | Despite widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans . despite their widespread adoption in society, chatbots have yet not shown natural chat capability . |
| Approach: | They propose a causality-enhanced method to enhance the impact of causality at the utterance level in training neural conversation models. |
| Outcome: | The proposed method improves diversity and agility of loss functions and still needs improvement . the proposed method is based on a CausalDialogue dataset . |
INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback (2023.emnlp-main)
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| Challenge: | Existing methods to evaluate the quality of language generation do not provide explicit explanation of their verdicts. |
| Approach: | They propose a fine-grained explainable evaluation metric for text generation that harnesses human instruction and implicit knowledge of GPT-4 to fine-tune it. |
| Outcome: | The proposed model outperforms all other unsupervised metrics on translation, captioning, data-to-text, and commonsense generation tasks. |
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes (2023.acl-long)
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| Challenge: | Existing learned metrics perform unsatisfactory across text generation tasks or require human annotations for training on specific tasks. |
| Approach: | They propose a self-supervised approach to train a model-based metric for text generation evaluation using sentences retrieved from a corpus. |
| Outcome: | The proposed model outperforms all prior unsupervised metrics on four text generation evaluation benchmarks, with an average Kendall improvement of 0.158. |
Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation (2026.findings-acl)
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| Challenge: | Existing methods for long-form complex narrative generation struggle to maintain global narrative coherence and logical consistency. |
| Approach: | They propose a framework that performs narrative planning on structural graph representations instead of direct sequential text representations. |
| Outcome: | The proposed model outperforms representative baselines across diverse scenarios. |
Uncovering Factor-Level Preference to Improve Human-Model Alignment (2025.findings-emnlp)
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| Challenge: | Large language models exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. |
| Approach: | They propose a framework to uncover and measure factor-level preference alignment of humans and large language models (LLMs) |
| Outcome: | The proposed framework uncovers and measures factor-level preference alignment of humans and large language models. |
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)
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| Challenge: | Existing tools for text-to-image synthesis can visualize machine imaginations for a given context. |
| Approach: | They propose a framework that uses machine-generated images to guide language models in open-ended text generation. |
| Outcome: | The proposed framework is effective on open-ended text generation tasks while showing minor degeneration. |
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers (2023.eacl-main)
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| Challenge: | Efforts to debias NLI have led to datasets that exhibit different kinds of bias than those shown before. |
| Approach: | They propose a new technique to detect and reduce single sentence label leakage . leakage is a problem with many modern NLI datasets, they argue . future work must prioritize reducing this problem, they write . |
| Outcome: | a new model-driven technique can detect leakage and detect subpopulations in the datasets which exhibit it . the proposed technique is based on the progressive evaluation of cluster outliers (PECO) . it allows objective measurement of leakage, and automatic detection of subpopulations in the data which exhibit leakage. |
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback (2024.findings-naacl)
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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. |
MotifAgent: Learning Molecular Assembly through Multi-Agent Collaboration for Chemical Language Understanding (2026.findings-acl)
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| Challenge: | Existing approaches to molecular understanding are limited to static motif recognition without understanding connection rules governing how motifs assemble into valid topological structures. |
| Approach: | They propose a multi-agent reinforcement learning framework inspired by emergent collective intelligence to solve a problem where each motif is represented by an agent sharing a common LLM backbone. |
| Outcome: | Extensive experiments show that the proposed framework surpasses specialized expert models in molecular understanding tasks. |
BPO: Staying Close to the Behavior LLM Creates Better Online LLM Alignment (2024.emnlp-main)
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| Challenge: | Existing offline DAP methods for aligning large language models with human preference are computationally expensive due to their two-stage training pipeline that consists of a reward modeling phase. |
| Approach: | They propose to align large language models to human desiderata from offline preference datasets by using an online approach. |
| Outcome: | The proposed approach improves performance across a wide range of tasks when training with the same amount of preference data. |