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There is a consensus that instruction fine-tuning of LLMs requires high-quality data, but what are they? LIMA (NeurIPS 2023) and AlpaGasus (ICLR 2024) are state-of-the-art methods for selecting such high-quality examples, either via manual curation or using GPT-3.5-Turbo as a quality scorer.
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Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2021
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Chen, M., Chu, Z., Wiseman, S., and Gimpel, K · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
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Instruction mining: When data mining meets large language model finetuning, 2023
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Alpagasus: Training a better alpaca with fewer data
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Orca: Progressive learning from complex explanation traces of gpt-4
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OpenAI · 2023
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Direct preference optimization: Your language model is secretly a reward model
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
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The false promise of imitating proprietary llms
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Neftune: Noisy embeddings improve instruction finetuning
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Evaluating large language models at evaluating instruction following
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A preliminary study of the intrinsic relationship between complexity and alignment
Zhao, Y., Yu, B., Hui, B., Yu, H., Huang, F., Li, Y., and Zhang, N. L · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
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LIMA: Less is more for alignment
Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., YU, L., Zhang, S., Ghosh, G., Lewis, M., Zettlemoyer, L., and Levy, O · 2023
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Sukhbaatar, S., Xu, J., and Weston, J · 2024
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