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We introduce Baichuan Alignment, a detailed analysis of the alignment techniques employed in the Baichuan series of models.
Rank analysis of incomplete block designs: I. the method of paired comparisons
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Proximal policy optimization algorithms, 2017
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Improving language understanding by generative pre-training
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
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Language models are few-shot learners
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Measuring massive multitask language understanding
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Approximating kl divergence, 2020
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Evaluating large language models trained on code
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Training verifiers to solve math word problems
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Measuring mathematical problem solving with the math dataset
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Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation
Yu Sun, Shuohuan Wang, Shikun Feng, Siyu Ding, Chao Pang, Junyuan Shang, Jiaxiang Liu, Xuyi Chen, Yanbin Zhao, Yuxiang Lu, et al · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan · 2022
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Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Self-consistency improves chain of thought reasoning in language models
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Learning from mistakes makes llm better reasoner
Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, and Weizhu Chen · 2023
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
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Qwen technical report, 2023
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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 · 2023
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Contrastive chain-of-thought prompting
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks
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Self-alignment with instruction backtranslation
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One shot learning as instruction data prospector for large language models
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Wei Liu, Weihao Zeng, Keqing He, Yong Jiang, and Junxian He · 2023
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Gpqa: A graduate-level google-proof q&a benchmark
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From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning
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Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model
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