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Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment., , , , , , , , , and 2 other author(s). CoRR, (2024)Exploring the Universal Vulnerability of Prompt-based Learning Paradigm., , , , and . NAACL-HLT (Findings), page 1799-1810. Association for Computational Linguistics, (2022)Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models., , , , , , , , and . CoRR, (2024)INTERVENOR: Prompt the Coding Ability of Large Language Models with the Interactive Chain of Repairing., , , , , , and . CoRR, (2023)A Close Look into the Calibration of Pre-trained Language Models., , , , and . CoRR, (2022)Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP., , , , , , and . EMNLP, page 11222-11237. Association for Computational Linguistics, (2022)Decoder Tuning: Efficient Language Understanding as Decoding., , , , , and . ACL (1), page 15072-15087. Association for Computational Linguistics, (2023)RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback., , , , , , , , , and 1 other author(s). CoRR, (2023)Revisiting Out-of-distribution Robustness in NLP: Benchmarks, Analysis, and LLMs Evaluations., , , , , , , , and . NeurIPS, (2023)Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations., , , , , , , , and . CoRR, (2023)