Papers with checkpoint averaging
Revisiting Checkpoint Averaging for Neural Machine Translation (2022.findings-aacl)
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| Challenge: | Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models. |
| Approach: | They propose to use checkpoint averaging to increase model performance . they also propose to calculate weighted average instead of simple mean . |
| Outcome: | The proposed method is widely adopted in neural machine translation research. |
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging (2023.acl-long)
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| Challenge: | Massively multilingual language models have shown strong performance in zero-shot (ZS-XLT) and few-shot cross-lingual transfer setups where models are fine-tuned on task data in a source language are transferred without any or with only a few annotated instances to the target language(s). |
| Approach: | They propose a method that averages different checkpoints during task fine-tuning to improve model robustness. |
| Outcome: | The proposed method overestimates model performance in cross-lingual transfer setups where models are evaluated at checkpoints that generalize best to validation instances in the target languages. |
Instability in Downstream Task Performance During LLM Pretraining (2025.findings-emnlp)
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| Challenge: | a study of large language models shows that task scores fluctuate throughout training . |
| Approach: | They empirically analyze the stability of downstream task performance in an LLM . |
| Outcome: | The proposed methods improve performance stability without changes to the training procedure. |