Papers by Shihao Gao
K-order Ranking Preference Optimization for Large Language Models (2025.findings-acl)
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| Challenge: | Existing list-wise methods focus on optimizing list ranking consistency for LLMs to improve ranking abilities. |
| Approach: | They propose to extend the Plackett-Luce model to accommodate top-K ranking by extending the DPO’s Plact-Lucer model to dynamically determine appropriate K for different samples. |
| Outcome: | The proposed model can be extended to accommodate top-K ranking and improve training efficiency. |
RACQC: Advanced Retrieval-Augmented Generation for Chinese Query Correction (2025.findings-emnlp)
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Jinbo Su, Lingzhe Gao, Wei Li, Shihao Liu, Haojie Lei, Xinyi Wang, Yuanzhao Guo, Ke Wang, Daiting Shi, Dawei Yin
| Challenge: | Large language models (LLMs) exhibit remarkable capabilities across many tasks, but face critical challenges in the CSC scenario: (1) poor generalization to rare entities in open-domain searches; and (2) failure to adapt to temporal entity variations due to static parameters, resulting in serious over-correction issues. |
| Approach: | They propose a Chinese Spelling Check system with RAG and multi-task learning that integrates dynamic knowledge retrieval and entity-centric RAG to address rare entities. |
| Outcome: | The proposed system outperforms existing baselines in the CSC task and achieves a maximum improvement of +9.92% on the search scenario benchmark and +3.2% on the general-domain dataset. |
DASA-Trans-STM: Adaptive Efficient Transformer for Short Text Matching using Data Augmentation and Semantic Awareness (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shown impressive versatility across various tasks. |
| Approach: | They propose a novel adaptive Transformer for Chinese short text matching using data augmentation and semantic awareness. |
| Outcome: | The proposed model can deal with word ambiguity in Chinese on four available datasets. |
Free your mouse! Command Large Language Models to Generate Code to Format Word Documents (2024.emnlp-main)
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| Challenge: | Recent LLMs have significantly improved code generation, making it increasingly accessible to users. |
| Approach: | They propose an automatic document formatting method, Text-to-Format, driven by various prompting strategies and a high-quality dataset DocFormEval data. |
| Outcome: | The proposed method improves the efficiency and experience of users in formatting the document and improves document formatting task. |