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How Many Demonstrations Do You Need for In-context Learning?, , , and . EMNLP (Findings), page 11149-11159. Association for Computational Linguistics, (2023)OPTune: Efficient Online Preference Tuning., , , , , , , , and . CoRR, (2024)Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement., , , , , , , , , and 1 other author(s). CoRR, (2024)Particle-based Energetic Variational Inference., , , and . CoRR, (2020)Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features., , , , , , and . ICLR, OpenReview.net, (2022)Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements., , , and . CoRR, (2024)GenQA: Generating Millions of Instructions from a Handful of Prompts., , , , , , and . CoRR, (2024)GOAT: A Global Transformer on Large-scale Graphs., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 17375-17390. PMLR, (2023)PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer., , , and . EMNLP, page 13512-13525. Association for Computational Linguistics, (2023)Multi-Objective Linguistic Control of Large Language Models., , and . CoRR, (2024)