FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation (2025.findings-emnlp)
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| Challenge: | Using large language models to generate meaningful tests is expensive and time-consuming . |
| Approach: | They propose a data augmentation technique that incorporates valid testing semantics and diverse coverage-guided inputs into large language models. |
| Outcome: | The proposed technique improves performance over the baselines by incorporating valid testing semantics and providing diverse coverage-guided inputs. |
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| Challenge: | Semantic understanding of programs has attracted great attention in the community . large language models (LLMs) are capable of learning contextual information from data at scale . |
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Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
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Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation (2022.acl-long)
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| Challenge: | Neural machine translation (NMT) tasks require large amounts of parallel data to augment training. |
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| Challenge: | Existing languages have syntactic representations of code to improve code intelligence, but they are difficult to learn from code. |
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| Challenge: | Prior approaches to synthesis use few-shot prompting, which relies on the LLM’s parametric knowledge to generate usable examples. |
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| Challenge: | Existing methods for jailbreaking large-language models are limited by their limitations . authors present a mutation-based fuzzing technique that generates effective jailbreaking templates . |
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Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks. |
| Approach: | They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity. |
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Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization (D19-1)
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| Challenge: | Existing studies measure the superiority of DA methods in terms of their performance on a specific test set, but some do not exhibit consistent improvements across translation tasks. |
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CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP (2024.findings-naacl)
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| Challenge: | a low-resource dataset is limited in training data, so generating task-specific data is challenging. |
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HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability (2021.acl-long)
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| Challenge: | Using data augmentation to fine-tune pre-trained models with task-specific data has been shown to be ineffective and redundant during fine-timing. |
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