Low-Rank Adaptation for Multilingual Summarization: An Empirical Study (2024.findings-naacl)
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| Challenge: | Pre-trained Large Language Models have significantly advanced NLP, but their ever-increasing size poses significant challenges for conventional fine-tuning. |
| Approach: | They investigate the potential of Low-Rank Adaptation (LoRA) in multilingual summarization, a task that is challenging and relatively unexplored. |
| Outcome: | The proposed method outperforms full fine-tuning and cross-lingual transfer strategies in multilingual summarization tasks. |
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| Challenge: | Low-rank adaptation (LoRA) is an efficient approach for adapting large language models (LLMs) but many of the weights in these matrices are redundant, leading to inefficiencies in parameter utilization. |
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