Papers by Xinshu Shen

4 papers
Generative Gamer: Learning Equilibrium Strategy by LLM-driven Dynamic Deduction (2026.acl-long)

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Challenge: Large Language Models (LLMs) falter in domains requiring deep strategic reasoning.
Approach: They propose a framework that trains LLMs to reason like an expert player . they propose action pruning based on policy confidence, state pruning via value estimation and branch pruning inspired by alpha-beta principles to train the model effectively.
Outcome: Experiments on Tic-Tac-Toe and Leduc Poker show that GenGamer significantly improves the strategic capabilities of large language models.
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.
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.
TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)

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Challenge: Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback.
Approach: They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations.
Outcome: The proposed method significantly improves both automatic and human evaluations across four diverse LLMs.

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