Papers by Yuzhang Lin
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)
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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang
| Challenge: | Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs). |
| Approach: | They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively. |
| Outcome: | The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score. |
LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration (2025.naacl-long)
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| Challenge: | Power distribution network reconfiguration is crucial for maintaining operational efficiency, reliability and adaptability in modern power networks. |
| Approach: | They propose a deep learning-based approach to solve a distribution network reconfiguration problem using inputs from a LLM. |
| Outcome: | The proposed model generates optimal configurations minimizing system loss for five individual and a combined test dataset. |
LARA: LLM-based Agile Power Distribution Network Restoration from Disastrous Events (2026.findings-eacl)
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| Challenge: | a large language model generates high-level restoration plans over a compact catalogue of feasible actions. |
| Approach: | They propose a method that generates restoration plans over a catalogue of feasible actions. |
| Outcome: | The proposed model outperforms a time-capped solver on an IEEE 13-node power distribution feeder by 13% while using less than 1% of its wall-clock runtime. |