Papers with full finetuning
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model (2020.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) generate derivationally complex words, but it is unclear what they learn about other aspects of language. |
| Approach: | They propose to use BERT to examine its derivational capabilities in different settings, from unmodified pretrained models to full finetuning. |
| Outcome: | The proposed model outperforms the state-of-the-art in derivation generation. |
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |
A Semantic-Aware Layer-Freezing Approach to Computation-Efficient Fine-Tuning of Language Models (2025.findings-acl)
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| Challenge: | Existing work on how to finetune but neglects the issue of where to fine-tune language models is expensive. |
| Approach: | They propose to use transition traces of latent representation to compute deviations (or loss) and then estimate the gain of each layer in reducing deviation (or gain). |
| Outcome: | The proposed approach outperforms baseline methods and is cost-benefit balanced. |
ApiQ: Finetuning of 2-Bit Quantized Large Language Model (2024.emnlp-main)
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| Challenge: | Memory-efficient finetuning of large language models (LLMs) has attracted huge attention with the increasing size of LLMs due to the constraints posed by GPU memory limitations and the effectiveness of these methods compared to full finetune. |
| Approach: | They propose a memory-efficient finetuning framework called ApiQ to restore lost information from quantization by initializing LoRA components and quantizing weights of LLMs. |
| Outcome: | The proposed framework maintains the original LLM’s activation precision while mitigating error propagation from shallower into deeper layers. |