Papers by Shiping Gao
Edit-Wise Preference Optimization for Grammatical Error Correction (2025.coling-main)
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| Challenge: | Large language models (LLMs) have been successful in grammatical error correction (GEC) but their strengths have yet to be fully demonstrated in GEC . |
| Approach: | They propose a method to optimize grammatical errors by assigning higher reward weights to edit tokens during preference optimization. |
| Outcome: | The proposed method outperforms baselines on English and Chinese datasets and achieves state-of-the-art performance. |
Self-Evolution Fine-Tuning for Policy Optimization (2024.findings-emnlp)
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| Challenge: | Recent years have showcased the remarkable capabilities and performance of large language models (LLMs) across a broad range of tasks. |
| Approach: | They propose supervised fine-tuning (SEFT) for LLM alignment to eliminate the need for annotated samples while retaining the stability and efficiency of SFT. |
| Outcome: | The proposed method eliminates the need for annotated samples while maintaining the stability and efficiency of SFT. |