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A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning., , , , , и . AACL/IJCNLP (1), стр. 806-822. Association for Computational Linguistics, (2022)Learning Universal Semantic Correspondences with No Supervision and Automatic Data Curation., , и . ICCV (Workshops), стр. 933-943. IEEE, (2023)What does CLIP know about a red circle? Visual prompt engineering for VLMs., , и . ICCV, стр. 11953-11963. IEEE, (2023)Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets., , , , , и . CoRR, (2023)What an Elegant Bridge: Multilingual LLMs are Biased Similarly in Different Languages., и . CoRR, (2024)BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology., , , , , и . EMNLP, стр. 2676-2694. Association for Computational Linguistics, (2023)HelloFresh: LLM Evalutions on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits., , , , , и . ACL (Findings), стр. 12702-12716. Association for Computational Linguistics, (2024)BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology., , , , , , и . CoRR, (2023)Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models., , , , , , , и . NeurIPS, стр. 2611-2624. (2021)SHIC: Shape-Image Correspondences with no Keypoint Supervision., , и . CoRR, (2024)