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. |
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