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As instruction-tuned large language models (LLMs) evolve, aligning pretrained foundation models presents increasing challenges.
A tutorial on principal component analysis
Jonathon Shlens. 2014 · 2014
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2015 · 2015
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017
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Predictive uncertainty estimation via prior networks
Andrey Malinin and MarkJ.F. Gales. 2018 · 2018
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A simple baseline for bayesian uncertainty in deep learning
Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. 2019 · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E. Hinton. 2019 · 2019
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Uncertainty-aware curriculum learning for neural machine translation
Yikai Zhou, Baosong Yang, Derek F. Wong, Yu Wan, and Lidia S. Chao. 2020 · 2020
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In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning
Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S. Rawat, and Mubarak Shah. 2021 · 2021
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Fast multi-view clustering via ensembles: Towards scalability, superiority, and simplicity
Dong Huang, Chang-Dong Wang, and Jian-Huang Lai. 2022 · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
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Effect of scale on catastrophic forgetting in neural networks
Vinay Venkatesh Ramasesh, Aitor Lewkowycz, and Ethan Dyer. 2022 · 2022
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UCL-AST: active self-training with uncertainty-aware clouded logits for few-shot text classification
Yi Xu, Jie Hu, Zhiqiao Gao, and Jinpeng Chen. 2022 · 2022
Cited alongside, same era.
Catastrophic forgetting in deep learning: A comprehensive taxonomy
Everton Lima Aleixo, Juan Gabriel Colonna, Marco Cristo, and Everlandio Fernandes. 2023 · 2023
Cited alongside, same era.
Alpacafarm: A simulation framework for methods that learn from human feedback
Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
Cited alongside, same era.
Mistral 7b
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
Cited alongside, same era.
Reinforcement learning with human feedback: Learning dynamic choices via pessimism
Zihao Li, Zhuoran Yang, and Mengdi Wang. 2023 · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Llama Team. 2023 · 2023
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2023 · 2023
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Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2023 · 2023
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Agenttuning: Enabling generalized agent abilities for llms
Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, and Jie Tang. 2023 · 2023
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GPTBIAS: A comprehensive framework for evaluating bias in large language models
Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin, and Mykola Pechenizkiy. 2023 · 2023
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What makes good data for alignment? A comprehensive study of automatic data selection in instruction tuning
Wei Liu, Weihao Zeng, Keqing He, Yong Jiang, and Junxian He. 2023 · 2023
Cited alongside, same era.
Qingyu Lu, Baopu Qiu, Liang Ding, Kanjian Zhang, Tom Kocmi, and Dacheng Tao. 2023 · 2023
Cited alongside, same era.
An empirical study of catastrophic forgetting in large language models during continual fine-tuning
Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. 2023 · 2023
Cited alongside, same era.
GPT-4 technical report
OpenAI. 2023 · 2023
Cited alongside, same era.
Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
Cited alongside, same era.
Evaluation metrics in the era of GPT-4: reliably evaluating large language models on sequence to sequence tasks
Andrea Sottana, Bin Liang, Kai Zou, and Zheng Yuan. 2023 · 2023
Cited alongside, same era.
Token-level self-evolution training for sequence-to-sequence learning
Keqin Peng, Liang Ding, Qihuang Zhong, Yuanxin Ouyang, Wenge Rong, Zhang Xiong, and Dacheng Tao. 2023a
Cited in the paper.
Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, and Xuanjing Huang. 2023 · 2023
Later among the works it cites.
LIMA: less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. 2023 · 2023
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Improving code generation by dynamic temperature sampling
Yuqi Zhu, Jia Allen Li, Ge Li, Yunfei Zhao, Jia Li, Zhi Jin, and Hong Mei. 2023 · 2023
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Deepseek LLM: scaling open-source language models with longtermism
Deepseek Team. 2024 · 2024
Closest in time.
Take care of your prompt bias! investigating and mitigating prompt bias in factual knowledge extraction
Ziyang Xu, Keqin Peng, Liang Ding, Dacheng Tao, and Xiliang Lu. 2024 · 2024
Closest in time.