| Challenge: | Existing noise mitigation methods face limitations due to their task-specific design, model dependency, and significant computational overhead. |
| Approach: | They propose a model-agnostic noise label mitigation paradigm that uses an estimator model and a scoring function to assess the noise level of input pairs. |
| Outcome: | The proposed method is superior to existing methods in training tasks and tasks. |
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Learning on Imbalanced Noisy Data via Debiased Sample Selection and LLM-Driven Annotation (2026.findings-acl)
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| Challenge: | Existing approaches to learning with noisy labels are prone to selection bias and training bias . obtaining large-scale high-quality datasets is expensive and time-consuming in practical scenarios . |
| Approach: | They propose an imbalanced learning with noisy labels task to let model learn from noisy labels . they first conduct debiased sample selection to better separate clean samples from noisy samples . then they feed selected clean samples to active annotator large language models for re-annotating noisy samples. |
| Outcome: | The proposed method is superior to existing methods on synthetic and real-world datasets. |
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. |
| Approach: | They propose a framework that decouples sample selection from model training by introducing clean and noisy LoRA. |
| Outcome: | The proposed framework decouples sample selection from model training. |
End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)
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| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
| Approach: | They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets. |
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On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning (2021.naacl-main)
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| Challenge: | PTLMs can exhibit biases against protected groups in a host of modeling tasks . but, fine-tuned LMs may propagate bias to downstream classifiers . |
| Approach: | They propose to use upstream bias mitigation techniques to reduce bias on downstream tasks by fine-tuning an upstream model and applying it to a downstream model. |
| Outcome: | The proposed model reduces bias on hate speech detection, toxicity detection and coreference resolution tasks over bias factors. |
Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning (2024.acl-long)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) has enabled efficient optimization of cumbersome language models in real-world environments. |
| Approach: | They propose a routing-based PEFT approach that adaptively activates PEFT modules. |
| Outcome: | The proposed method is more sensitive to noise interference than other methods. |
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLMs-Powered Assistance (2024.acl-long)
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| Challenge: | Existing methods for learning from noisy labels are difficult to improve . existing methods identify noisy labels and use active learning to query experts . |
| Approach: | They propose a collaborative learning framework to combine LLMs and small models for learning from noisy labels. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on synthetic and real-world noise datasets. |
Open-DeBias: Toward Mitigating Open-Set Bias in Language Models (2025.findings-emnlp)
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| Challenge: | Existing approaches to addressing harmful biases in LLMs are limited to predefined categories . a novel, data-efficient, and parameter-efficient debiasing method is proposed to mitigate existing social and stereotypical biase . |
| Approach: | They propose an open-set bias detection and mitigation method to address harmful biases in text-based QA. |
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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. |
Noisy Label Regularisation for Textual Regression (2022.coling-1)
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| Challenge: | Existing methods to regularise noisy labels are ineffective in the face of noisy data. |
| Approach: | They propose a method that regularises noisy labels and prevents error propagation from the input layer. |
| Outcome: | The proposed method regularises noisy labels and improves generalisation performance over real-world human-disagreement annotations and randomly-corrupted and data-augmented labels. |
When and Why Does Bias Mitigation Work? (2023.findings-emnlp)
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| Challenge: | Neural models exploit shallow surface features to perform language understanding tasks, rather than learning the deeper language understanding and reasoning skills that practitioners desire. |
| Approach: | They propose to use model debiasing techniques to pressure models away from spurious features and to use them to learn useful representations instead. |
| Outcome: | The proposed methods increase models' reliance on hidden biases instead of learning robust features that help them solve a task. |