Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets (2023.emnlp-main)
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| Challenge: | Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art in NLP, however, data quality might have already become the bottleneck to unlock further gains. |
| Approach: | They propose a general method for improving model performance in the presence of noisy training data based on self-influence and bandit curriculum learning. |
| Outcome: | The proposed method improves model performance in machine translation, question answering and text classification, building up on approaches to self-influence calculation and automated curriculum learning. |
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| Challenge: | Existing approaches to interpret black-box models to learn spurious correlations are not well understood. |
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Changing the World by Changing the Data (2021.acl-long)
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| Challenge: | a new paper argues that data curation is already happening, and it is changing the world . social biases and spurious patterns are attracting more attention in NLP models . |
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| Challenge: | Modern deep learning models for NLP are notoriously opaque, and this has motivated efforts to design example-specific approaches to interpret such models. |
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Influence Scores at Scale for Efficient Language Data Sampling (2023.emnlp-main)
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| Challenge: | Recent studies have shown that ML models can be fine-tuned on as much data as possible without degradation in performance metrics. |
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On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations (2022.findings-naacl)
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| Challenge: | Recent work shows that deep learning models are sensitive to low-level correlations between simple features and specific output labels, leading to over-fitting and lack of generalization. |
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A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
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Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
| Challenge: | Data augmentation is a field of research that has been underexplored due to the discrete nature of language data. |
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
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Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities (2025.findings-emnlp)
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| Challenge: | Influence-based methods show promise in achieving (1), but often struggle with (2) . data selection is often biased towards high-influence tasks, harming performance on them . |
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Do Influence Functions Work on Large Language Models? (2025.findings-emnlp)
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| Challenge: | Influence functions are important for quantifying the impact of individual training data points on a model’s predictions. |
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Curating Datasets for Better Performance with Example Training Dynamics (2023.findings-acl)
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| Challenge: | Existing methods to improve data quality but rely on data quantity to improve performance are not effective. |
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