| Challenge: | Despite their general capabilities, LLMs struggle on biomedicalNER tasks due to specialized terminology and lack of training data. |
| Approach: | They propose a new knowledge augmentation approach which incorporates definitions of relevant concepts on-the-fly. |
| Outcome: | The proposed approach improves performance on biomedicalNER tasks by 15% (on average) The proposed method outperforms fine-tuned language models in few-shot settings. |
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| Challenge: | Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition, but this approach is costly and unreliable as it relies on external models or manual effort for rewriting. |
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| Challenge: | specialized fields such as science and biology face significant challenges due to the scarcity of quality data. |
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Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges (2024.findings-acl)
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LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement (2024.findings-acl)
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Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael Mahoney, Kurt Keutzer, Amir Gholami
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