Papers by Chenyue Zhou

2 papers
TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action (2026.findings-eacl)

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Challenge: Humanitarian Mine Action (HMA) authorities publish large amount of life-saving operational knowledge, but much remains locked away in unstructured reports.
Approach: They propose a dataset, evaluation framework and ontology-guided large language model pipeline for knowledge extraction from text in the HMA domain.
Outcome: The proposed framework improves extraction accuracy by 44.2% and reduces hallucinations by 22.5% . the proposed framework can be used to analyze human-annotated triples and an LLM-as-Judge protocol .
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)

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Challenge: Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations.
Approach: They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency.
Outcome: The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types.

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