Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models (2025.findings-emnlp)
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| Challenge: | Existing noise detection techniques for autoencoder models do not generalize to ArLMs due to differences in learning dynamics. |
| Approach: | They propose a method that leverages training dynamics to rank datapoints from easy-to-learn to hard-tolear . TDRanker achieves at least 2x faster denoising than previous techniques . |
| Outcome: | The proposed method demonstrates robustness across multiple model architectures and noise levels. |
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| Challenge: | Large-scale datasets in the real world often contain label noise, which can cause model overfitting and degrade generalization. |
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Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance (2023.findings-emnlp)
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| Challenge: | Pretrained Language Models (PLMs) are advanced but data labels are noisy due to the complex annotation process. |
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Weed Out, Then Harvest: Dual Low-Rank Adaptation is an Effective Noisy Label Detector for Noise-Robust Learning (2025.findings-acl)
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| Challenge: | Experimental results show that PEFT can fine-tune language models without relying on perfectly labeled datasets. |
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FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)
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| Challenge: | Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation. |
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Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models (2024.findings-eacl)
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| Challenge: | Existing methods for text classification using autoregressive language models are limited . authors propose a novel technique for text classification using autoreregressives . |
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| Challenge: | Existing data filtering methods rely on coarse-grained scores that lack granularity to identify nuanced semantic flaws. |
| Approach: | They propose a "Decomposition-then-Evaluation" paradigm that breaks model responses into constituent cognitive components. |
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Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)
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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang
| Challenge: | Existing approaches to reduce label noise rely on heuristics and sample losses. |
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MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
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Modeling Noise in Paraphrase Detection (2022.lrec-1)
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| Challenge: | Noisy labels in training data are challenging and can lead to incorrect decisions . large pre-trained language models have achieved great results in many NLP tasks . |
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NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)
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| Challenge: | Existing approaches to named entity recognition often contain a significant percentage of incorrect labels for entity types and boundary boundaries. |
| Approach: | They propose a noise-robust learning approach that learns from data with partially incorrect labels. |
| Outcome: | The proposed methods are based on simulated noise and are easier to handle than simulated real noise caused by human error or semi-automatic annotation. |