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From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework., , , , , , , , , and 3 other author(s). ACL (Findings), page 9607-9632. Association for Computational Linguistics, (2023)Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework., , , and . ACL (Findings), page 7132-7146. Association for Computational Linguistics, (2023)A Close Look into the Calibration of Pre-trained Language Models., , , , and . ACL (1), page 1343-1367. Association for Computational Linguistics, (2023)CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets., , , , , and . CoRR, (2023)A Single Transformer for Scalable Vision-Language Modeling., , , and . CoRR, (2024)Prudent Silence or Foolish Babble? Examining Large Language Models' Responses to the Unknown., , , , and . CoRR, (2023)Executable Code Actions Elicit Better LLM Agents., , , , , , and . ICML, OpenReview.net, (2024)Perplexed Messengers from the Cloud: Automated Security Analysis of Push-Messaging Integrations., , , , and . ACM Conference on Computer and Communications Security, page 1260-1272. ACM, (2015)Sedic: privacy-aware data intensive computing on hybrid clouds., , , , and . ACM Conference on Computer and Communications Security, page 515-526. ACM, (2011)Making Pre-trained Language Models both Task-solvers and Self-calibrators., , and . ACL (Findings), page 9845-9862. Association for Computational Linguistics, (2023)