Papers by Wanrong Zhang
Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models (2025.acl-long)
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| Challenge: | Prior work has shown that privacy leakage of parametric knowledge often occurs from memorized pre-training data. |
| Approach: | They propose a metric that builds on differential privacy to estimate the privacy leakage of contextual knowledge during decoding by comparing parametric and contextual knowledge. |
| Outcome: | The proposed method overestimates the privacy leakage of parametric knowledge while separating parametric and contextual knowledge. |
HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion (2023.acl-long)
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Wanrong He, Haoyu Dong, Yihuai Gao, Zhichao Fan, Xingzhuo Guo, Zhitao Hou, Xiao Lv, Ran Jia, Shi Han, Dongmei Zhang
| Challenge: | HermEs is a spreadsheet formula prediction language that is difficult for Excel users without programming experience to master. |
| Approach: | They propose a hierarchical approach to formula prediction via HiEraRchical forMulet ExpanSion . they propose generating formulas in a fixed order using hierarchically generated formulas . |
| Outcome: | The proposed approach improves formula prediction accuracy by guaranteeing correct grammar and streamlining token-level decoding with high-level Formulet. |
TokenShapley: Token Level Context Attribution with Shapley Value (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have strong capabilities in in-context learning, but verifying the correctness of their generated responses remains a challenge. |
| Approach: | They propose a token-level attribution method that combines Shapley value-based data attribution with KNN-based retrieval techniques to improve attribution accuracy. |
| Outcome: | TokenShapley outperforms state-of-the-art methods on four benchmarks . it achieves an 11–23% improvement in accuracy on the benchmarks. |