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. |
| Approach: | They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs. |
| Outcome: | The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version. |
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| Challenge: | Currently, large language models are fine-tuned using expensive human-annotated data or GPT-4 generated data. |
| Approach: | They propose to use web-crawled data to train a language model on a smaller set of data . their results show that the model can convert web data with irregular formats into high-quality ones . |
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GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)
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| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
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Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice? (2024.emnlp-main)
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| Challenge: | Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data. |
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LLM-powered Data Augmentation for Enhanced Cross-lingual Performance (2023.emnlp-main)
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| Challenge: | Existing training data for multilingual commonsense reasoning datasets is limited. |
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Reformatted Alignment (2024.findings-emnlp)
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| Challenge: | Current methods to improve data quality are labor-intensive or prone to factual errors caused by LLM hallucinations. |
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Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
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| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
| Approach: | They propose to review parameter-efficient fine-tuning techniques that lower training and deployment costs and domain and cross-lingual adaptation methods for both encoder and decoder models. |
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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. |
| Approach: | They propose a framework for fine-tuning PLMs using noisy labels that incorporates guidance from Large Language Models like ChatGPT. |
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Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection (2026.findings-eacl)
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| Challenge: | Existing methods to improve robustness require changing the fine-tuning process or large-scale data augmentation, which are infeasible or cost prohibitive for closed-source models. |
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LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (2023.emnlp-main)
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Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Lee
| Challenge: | Large language models (LLMs) have shown unprecedented performance across various tasks. |
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Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy (2025.coling-main)
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| Challenge: | Existing methods for text classification based on large language models are difficult to apply directly to solve. |
| Approach: | They propose a data quality enhancement method to improve LLMs' performance in classification tasks by using a greedy algorithm to select data and then performing fine-tuning. |
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