Papers by Moiz Ali
Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs (2025.findings-emnlp)
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| Challenge: | a study shows that comprehension-intensive fine-tuning tasks retain knowledge longer . however, all models exhibit significant performance drops when applying injected knowledge in broader contexts . |
| Approach: | study: comprehension-intensive fine-tuning tasks achieve higher knowledge retention rates . larger models show improved retention across all task types, study finds . |
| Outcome: | a new study shows that comprehension-intensive fine-tuning tasks retain knowledge better than mapping-oriented tasks despite exposure to identical factual content. |
Multitask-Bench: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment analysis. |
| Approach: | They propose to fine-tune LLMs on benign (non-harmful) data to ensure safe outputs. |
| Outcome: | The proposed model reduces attack success rates across a range of tasks without compromising its usefulness. |