Papers by Xiaopeng Wu

5 papers
CEAMC: Corpus and Empirical Study of Argument Analysis in Education via LLMs (2024.findings-emnlp)

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Challenge: Existing argument component classifications in education are simplistic and isolated, failing to capture the complete argument information.
Approach: They propose to annotate a manually annotated argument component classification dataset from authentic examination settings and to explore the performance of Large Language Models on CEAMC.
Outcome: The proposed dataset can be used to analyze argumentative essays in education.
Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method (2024.findings-emnlp)

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Challenge: Existing GEC datasets in Chinese fail to consider specific grammatical error types and overlook cross-sentence grammamatical errors.
Approach: They propose to use Chinese essay fluency assessment to assess essay fluencies along with coarse and fine-grained errors and corrections to improve explainability.
Outcome: The proposed dataset encapsulates essay fluency scores along with both coarse and fine-grained errors and corrections.
CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays (2024.findings-emnlp)

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Challenge: Existing rhetorical understanding and generation datasets focus on single coarse-grained categories or fine-grain categories, neglecting the intrinsic connections between different rhetorical devices.
Approach: They propose a Chinese Essay Rhetoric Dataset with four coarse-grained categories . they propose to treat these categories as separate sub-tasks, thereby improving writing skills .
Outcome: The proposed dataset improves the author's writing proficiency and language usage skills by recognizing and generating rhetorical sentences under given conditions.
Do not Abstain! Identify and Solve the Uncertainty (2025.acl-long)

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Challenge: Existing solutions rely on evasive responses when confronting uncertain scenarios.
Approach: They propose a benchmark to assess LLMs' ability to recognize and address uncertainty . they generate context-aware inquiries that highlight the confusing aspect of the original query .
Outcome: Experiments with ConfuseBench show that LLMs struggle to identify root cause of uncertainty and solve it.
A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis (2023.emnlp-main)

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Challenge: Existing research focuses on isolated dimensions of discourse coherence . Existing discourse cohesion analyses focus on isolated aspects of discourse .
Approach: They introduce a Chinese Essay Discourse Coherence Corpus (CEDCC) which integrates coherence grading, topical continuity, and discourse relations.
Outcome: The proposed dataset captures the subtleties of real-world texts and stimulates progress in Chinese discourse coherence analysis.

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