Papers with word substitution
Towards Improving Adversarial Training of NLP Models (2021.findings-emnlp)
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| Challenge: | Recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. |
| Approach: | They propose to use vanilla adversarial training to train NLP models using a word substitution attack optimized for vanilla adversary training. |
| Outcome: | The proposed approach improves model performance and standard accuracy and can defend against other types of word substitution attacks. |
Random Smooth-based Certified Defense against Text Adversarial Attack (2024.findings-eacl)
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| Challenge: | Textual adversarial examples train models on the worst-case text generated by substituting words in original texts with synonyms, but due to the discrete word embedding representations, the large search space hinders the robust training efficiency. |
| Approach: | They propose to treat the word substitution as a continuous perturbation on the word embedding representation and apply random smooth techniques to approximate the word replacement operation. |
| Outcome: | The proposed method outperforms conventional methods and improves the robustness in training. |
Certified Robustness to Word Substitution Attack with Differential Privacy (2021.naacl-main)
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| Challenge: | Recent studies have shown that adversarial examples can be easily fooled by DNNs, making the robustness and security of NLP models significantly important. |
| Approach: | They propose a differential privacy-based algorithm to achieve certified robustness against word substitution at- tacks in text classification via differential privacy. |
| Outcome: | The proposed model achieves higher accuracy and more than 30X efficiency improvement over existing defense algorithms. |
CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages (2025.emnlp-main)
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| Challenge: | Existing benchmarks for large language models (LLMs) are limited by their narrow language pairs and tasks, failing to adequately assess their code-mixing abilities. |
| Approach: | They propose a benchmark to assess large language models' (LLMs) code-mixing abilities that covers eight tasks and 18 languages from seven language families. |
| Outcome: | The proposed method combines word substitution with GPT-4 prompting to generate large-scale synthetic code-mixed texts. |
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions (2020.acl-main)
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| Challenge: | State-of-the-art NLP models can be fooled by human-unaware transformations such as synonymous word substitution. |
| Approach: | They propose a method that constructs a stochastic ensemble by applying random word substitutions on the input sentences and leverages the statistical properties to provably certify the robustness. |
| Outcome: | The proposed method outperforms state-of-the-art methods on IMDB and Amazon text classification tasks with practically meaningful certified accuracy. |
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have shown strong potential in various downstream tasks. |
| Approach: | They propose to model adversarial attack task as a sequential decision-making problem where the whole attack process is sequential with two decision- making problems, i.e., word finder and word substitution. |
| Outcome: | The proposed approach achieves the highest attack success rate with a comparable modification rate and semantic similarity to attack fine-tuned BERT. |
Learning to Substitute Words with Model-based Score Ranking (2025.naacl-long)
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| Challenge: | Experimental results show that the proposed approach outperforms both masked language models and large language models. |
| Approach: | They propose a model-based scoring approach to quantify sentence quality . they propose 'loss function' that optimizes alignment between model predictions and sentence scores . |
| Outcome: | The proposed approach outperforms masked language models and large language models in the quantitative analysis of word substitutions. |
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks, but their utilization carries inherent risks, including plagiarism and the dissemination of fake news. |
| Approach: | They propose to use a dataset to construct an AI-generated student essay that employs a range of text perturbation methods to evade detection. |
| Outcome: | The proposed methods evade detection and maintain quality of the generated essays while avoiding plagiarism and fake news. |