| Challenge: | supervised learning is a challenging process due to the huge number of parameter combinations. |
| Approach: | They present an example of parameter selection in supervised learning . authors use a set of frequently occurring labels without a parameter tuning . they say this illustrates the seriousness of parameter tuning in a supervised field . |
| Outcome: | The proposed study shows that without adequate attention, the research progress can be uncertain or even illusive. |
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| Challenge: | Recent research has illuminated the possibility of selective parameter-efficient fine-tuning, which retains the inference speed of the original model and comes at no additional computational cost. |
| Approach: | They propose to selectively update only a small subset of parameters during the fine-tuning process, keeping the remaining parameters frozen during training. |
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A Few More Examples May Be Worth Billions of Parameters (2022.findings-emnlp)
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| Challenge: | Recent work on few-shot learning for natural language tasks explores the dynamics of scaling up either the number of model parameters or labeled examples while controlling for the other variable by setting it to a constant. |
| Approach: | They explore the dynamics of scaling up the number of model parameters versus the number labeled examples across a wide variety of tasks. |
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Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
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Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN (2022.naacl-main)
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| Challenge: | XML-CNN has been a popular research topic in NLP due to its superior performance . however, the increasing complexity brings difficulties to ensure the true architectural progress . |
| Approach: | They propose to re-examine an influential multi-label text classification method . they propose suitable baselines for multi-level text classification tasks . |
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Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)
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| Challenge: | Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results . |
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Chasing Random: Instruction Selection Strategies Fail to Generalize (2025.findings-naacl)
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| Challenge: | Prior work has shown that language models can be tuned to follow user instructions using only a small set of high-quality instructions. |
| Approach: | They analyze popular selection strategies across different datasets and benchmarks to find out whether they generalize poorly. |
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Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning (2023.findings-acl)
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| Challenge: | Existing studies on parameter-efficient fine-tuning methods require additional measures after pre-training and before fine-uning. |
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Parameter-Efficient Tuning Makes a Good Classification Head (2022.emnlp-main)
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| Challenge: | In recent years, pretrained models revolutionized the paradigm of natural language understanding . but the final-layer output of the backbone, i.e. the input of the classification head, will change greatly during finetuning . |
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Does Localization Inform Unlearning? A Rigorous Examination of Local Parameter Attribution for Knowledge Unlearning in Language Models (2025.emnlp-main)
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| Challenge: | Recent studies emphasize localized unlearning, restricting parameter updates to specific regions to remove unrelated general knowledge. |
| Approach: | They revisit existing localized unlearning approaches and conduct experiments to evaluate their effectiveness. |
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Revisiting Parameter-Efficient Tuning: Are We Really There Yet? (2022.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) are used as backbones to be combined with additional parameters and finetuned on downstream tasks in an end-to-end manner. |
| Approach: | They propose to use a fraction of parameters to tune pretrained language models (PLMs) this is the first comprehensive investigation into the training and evaluation of PETuning methods. |
| Outcome: | The proposed methods have been validated and tested with a rigorous evaluation protocol and have shown that they are unstable and inconsistent. |