ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning (2024.emnlp-demo)
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| Challenge: | Existing frameworks for large language model embeddings have limited support for only a limited range of architectures and fine-tuning strategies. |
| Approach: | They propose a framework that enables bidirectional attention across various LLMs and supports a range of fine-tuning strategies. |
| Outcome: | The proposed framework enables bidirectional attention across various LLMs and supports a range of fine-tuning strategies. |
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| Challenge: | True. True. False |
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Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean (2024.lrec-main)
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ChangSu Choi, Yongbin Jeong, Seoyoon Park, Inho Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim
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