{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/valcat-generating-variable-length","title":"ValCAT: Generating Variable-Length Contextualized Adversarial Transformations using Encoder-Decoder","arxiv_id":null,"date":"2022-01-16","proceeding":"ACL ARR January 2022 1","authors":["Anonymous"],"abstract":"Adversarial samples are helpful to explore vulnerabilities in neural network models, improve model robustness, and explain their working mechanism. However, the adversarial texts generated by existing word substitution-based methods are trapped in a one-to-one attack pattern, which is inflexible and cramped. In this paper, we propose ValCAT, a black-box attack framework that misleads the language model by applying variable-length contextualized transformations to the original text. Experiments show that our method outperforms state-of-the-art methods on attacking several classification tasks and inference tasks. More comprehensive human evaluations demonstrate that ValCAT has a significant advantage in ensuring the fluency of the adversarial samples and achieves better semantic consistency. We release our code at https://github.com/linerxliner/ValCAT.","url_abs":"https://openreview.net/forum?id=fE6Md7R_vqA","url_pdf":"https://openreview.net/pdf?id=fE6Md7R_vqA","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"valcat-generating-variable-length","repo_url":"https://github.com/linerxliner/valcat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}