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Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs.
Rasley, J., Rajbhandari, S., Ruwase, O., He, Y.: Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3505–3506 (2020)
2020
Earlier work this paper cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al
2022
Earlier work this paper cites.
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al
2023
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2023
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Lian, W., Goodson, B., Pentland, E., Cook, A., Vong, C., ”Teknium”: OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces. HuggingFace (2023)
2023
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2023
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Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B.: Stanford Alpaca: An Instruction-following LLaMA model. GitHub (2023)
2023
Earlier work this paper cites.
Muennighoff, N., Liu, Q., Zebaze, A., Zheng, Q., Hui, B., Zhuo, T.Y., Singh, S., Tang, X., Werra, L.V., Longpre, S.: OctoPack: Instruction tuning code large language models. In: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following (2023)
2023
Cited alongside, same era.
Lee, A.N., Hunter, C.J., Ruiz, N.: Platypus: Quick, cheap, and powerful refinement of LLMs. In: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following (2023)
2023
Cited alongside, same era.
Qi, X., Zeng, Y., Xie, T., Chen, P.-Y., Jia, R., Mittal, P., Henderson, P.: Fine-tuning aligned language models compromises safety, even when users do not intend to! In: International Conference on Learning Representations (2024)
2024
Cited alongside, same era.
2024
Cited alongside, same era.
Hsu, C.-Y., Tsai, Y.-L., Lin, C.-H., Chen, P.-Y., Yu, C.-M., Huang, C.-Y.: Safe LoRA: The silver lining of reducing safety risks when finetuning large language models. In: Neural Information Processing Systems (2024)
2024
Later among the works it cites.
Dao, T.: FlashAttention-2: Faster attention with better parallelism and work partitioning. In: International Conference on Learning Representations (2024)
2024
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2025
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Choudhury, M., Elyoseph, Z., Fast, N.J., Ong, D.C., Nsoesie, E.O., Pavlick, E.: The promise and pitfalls of generative ai. Nature Reviews Psychology, 1–6 (2025)
2025
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Bianchi, F., Suzgun, M., Attanasio, G., Rottger, P., Jurafsky, D., Hashimoto, T., Zou, J.: Safety-tuned LLaMAs: Lessons from improving the safety of large language models that follow instructions. In: International Conference on Learning Representations (2024)
2024
Cited alongside, same era.
Peng, S., Chen, P.-Y., Hull, M.D., Chau, D.H.: Navigating the safety landscape: Measuring risks in finetuning large language models. In: Neural Information Processing Systems (2024)
2024
Cited alongside, same era.
2025
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Shen, H., Chen, P.-Y., Das, P., Chen, T.: SEAL: Safety-enhanced aligned LLM fine-tuning via bilevel data selection. In: International Conference on Learning Representations (2025)
2025
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