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Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment., , , , , , , , , и 2 other автор(ы). CoRR, (2024)Exploring the Universal Vulnerability of Prompt-based Learning Paradigm., , , , и . NAACL-HLT (Findings), стр. 1799-1810. Association for Computational Linguistics, (2022)Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models., , , , , , , , и . CoRR, (2024)Advancing LLM Reasoning Generalists with Preference Trees., , , , , , , , , и 5 other автор(ы). CoRR, (2024)INTERVENOR: Prompt the Coding Ability of Large Language Models with the Interactive Chain of Repairing., , , , , , и . CoRR, (2023)A Close Look into the Calibration of Pre-trained Language Models., , , , и . CoRR, (2022)Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP., , , , , , и . EMNLP, стр. 11222-11237. Association for Computational Linguistics, (2022)Decoder Tuning: Efficient Language Understanding as Decoding., , , , , и . ACL (1), стр. 15072-15087. Association for Computational Linguistics, (2023)RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback., , , , , , , , , и 1 other автор(ы). CoRR, (2023)Revisiting Out-of-distribution Robustness in NLP: Benchmarks, Analysis, and LLMs Evaluations., , , , , , , , и . NeurIPS, (2023)