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GOAT: A Global Transformer on Large-scale Graphs.

, , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 17375-17390. PMLR, (2023)

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GOAT: A Global Transformer on Large-scale Graphs., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 17375-17390. PMLR, (2023)Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery., , , , , and . CoRR, (2023)NEFTune: Noisy Embeddings Improve Instruction Finetuning., , , , , , , , , and 3 other author(s). CoRR, (2023)NEFTune: Noisy Embeddings Improve Instruction Finetuning., , , , , , , , , and 3 other author(s). ICLR, OpenReview.net, (2024)Transformers Can Do Arithmetic with the Right Embeddings., , , , , , , , , and 1 other author(s). CoRR, (2024)OPTune: Efficient Online Preference Tuning., , , , , , , , and . CoRR, (2024)A Watermark for Large Language Models., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 17061-17084. PMLR, (2023)Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs., , , , , , , , , and 1 other author(s). CoRR, (2024)On the Reliability of Watermarks for Large Language Models., , , , , , , , , and . ICLR, OpenReview.net, (2024)GenQA: Generating Millions of Instructions from a Handful of Prompts., , , , , , and . CoRR, (2024)