Papers by Gao yu Zhu
Detoxification for LLM: From Dataset Itself (2026.acl-long)
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| Challenge: | Existing methods for large language models focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. |
| Approach: | They propose to localize and rewrite toxic spans in raw corpora with SoCD, which guides an LLM to localized and preserving semantics while preserving toxicity. |
| Outcome: | The proposed method reduces TP from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20 on three LLMs. |
Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation (2025.naacl-industry)
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| Challenge: | Text2Sql is a task that translates natural language questions and database schemas into SQL queries. |
| Approach: | They employ pure fine-tuning strategy to reduce redundancy by using only 53% of the baseline prompt length to fine- tune the model. |
| Outcome: | The model outperforms the baseline model by 8.2% and 8.6% in Test-suite accuracy (TS) and exact-set-match accuracy (EM) under the most refined Spider dev set of prompts, the model achieves 73.5% and 75.4%, respectively, approaching state-of-the-art (SOTA) levels. |