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Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs).
Language models are few-shot learners
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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A survey on in-context learning
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Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, et al. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
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Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022 · 2022
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Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al. 2023 · 2023
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Soulchat: Improving llms’ empathy, listening, and comfort abilities through fine-tuning with multi-turn empathy conversations
Yirong Chen, Xiaofen Xing, Jingkai Lin, Huimin Zheng, and Zhenyu Wang. 2023b · 2023
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Chatlaw: Open-source legal large language model with integrated external knowledge bases
Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023 · 2023
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Ambiguity-aware in-context learning with large language models
Lingyu Gao, Aditi Chaudhary, Krishna Srinivasan, Kazuma Hashimoto, Karthik Raman, and Michael Bendersky. 2023 · 2023
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The false promise of imitating proprietary llms
Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023 · 2023
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2024 · 2024
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Xiezhi: An ever-updating benchmark for holistic domain knowledge evaluation
Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye, Lin Zhang, Jianchen Wang, Yixin Zhu, Sihang Jiang, Zhuozhi Xiong, Zihan Li, Weijie Wu, et al. 2024 · 2024
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Human-instruction-free llm self-alignment with limited samples
Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, and Yang Liu. 2024 · 2024
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The unlocking spell on base LLMs: Rethinking alignment via in-context learning
Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, Nouha Dziri, Melanie Sclar, Khyathi Chandu, Chandra Bhagavatula, and Yejin Choi. 2024 · 2024
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Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, and Noah A Smith. 2024 · 2024
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R-tuning: Teaching large language models to refuse unknown questions
Hanning Zhang, Shizhe Diao, Yong Lin, Yi R Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, and Tong Zhang. 2023 · 2023
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Pytorch fsdp: experiences on scaling fully sharded data parallel
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Maybe only 0.5% data is needed: A preliminary exploration of low training data instruction tuning
Hao Chen, Yiming Zhang, Qi Zhang, Hantao Yang, Xiaomeng Hu, Xuetao Ma, Yifan Yanggong, and Junbo Zhao. 2023a
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Rethinking data selection for supervised fine-tuning
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Principle-driven self-alignment of language models from scratch with minimal human supervision
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