Papers by Weizheng Wang

3 papers
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026.findings-acl)

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Challenge: Existing studies on prompt engineering have focused on optimizing models for performance under stylistic perturbations.
Approach: They conduct the first analysis of n-gram token-level mechanisms . they find that higher average performance is inherently associated with lower variance and greater stability.
Outcome: The proposed model reduces the variance of the generated code by 40% . the proposed model is based on a large-scale dataset of 132,000 prompt variants .
Joint Optimization of Training Data and Policy in RLHF (2026.findings-acl)

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Challenge: JODP optimizes policies on fixed training inputs, limiting the diversity of learning signals.
Approach: They propose a framework where policy generates improved variants of training problems to enhance its own learning.
Outcome: The proposed framework improves on safety alignment tasks by allowing 4B models to reach 8B model performance with less than 1% additional computational overhead.
DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

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Challenge: Dongba pictographic is the only pictograph script still in use in the world.
Approach: DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome: The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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