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The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Xiaodong Song. 2019 · 1906
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Privacy as contextual integrity
Helen Nissenbaum. 2004 · 2004
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Recipes for safety in open-domain chatbots
Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, and Emily Dinan. 2020 · 2010
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Privacy-preserving neural representations of text
Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Xiaodong Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2020
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2020 · 2020
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie C. Lin, Jacob Hilton, and Owain Evans. 2021 · 2021
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Selective differential privacy for language modeling
Weiyan Shi, Aiqi Cui, Evan Li, Ruoxi Jia, and Zhou Yu. 2021 · 2021
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Process for adapting language models to society (palms) with values-targeted datasets
Irene Solaiman and Christy Dennison. 2021 · 2021
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Challenges in detoxifying language models
Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John F. J. Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, and Po-Sen Huang. 2021 · 2021
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Large-scale differentially private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. 2022 · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, T. J. Henighan, Nicholas Joseph, Saurav Kadavath, John 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 B. Brown, Jack Clark, Sam McCandlish, Christopher Olah, Benjamin Mann, and Jared Kaplan. 2022 · 2022
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang. 2022 · 2022
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Glm: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022 · 2022
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2022 · 2022
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Teaching language models to support answers with verified quotes
Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, and Nathan McAleese. 2022 · 2022
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Training language models to follow instructions with human feedback
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 E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe. 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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Will we run out of data? an analysis of the limits of scaling datasets in machine learning
Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, and An Chang Ho. 2022 · 2022
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Poisoning language models during instruction tuning
Alexander Wan, Eric Wallace, Sheng Shen, and Dan Klein. 2023 · 2023
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On the robustness of chatgpt: An adversarial and out-of-distribution perspective
Jindong Wang, Hu Xixu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Wei Ye, Haojun Huang, Xiubo Geng, et al. 2023a · 2023
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Overcoming catastrophic forgetting in zero-shot cross-lingual generation
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Self-instruct: Aligning language model with self generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2022 · 2022
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Fldp: Flexible strategy for local differential privacy
Dan Zhao, Hong Chen, Suyun Zhao, Ruixuan Liu, Cuiping Li, and Xiaoying Zhang. 2022 · 2022
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Adversarial training for high-stakes reliability
Daniel M. Ziegler, Seraphina Nix, Lawrence Chan, Tim Bauman, Peter Schmidt-Nielsen, Tao Lin, Adam Scherlis, Noa Nabeshima, Ben Weinstein-Raun, Daniel Haas, Buck Shlegeris, and Nate Thomas. 2022 · 2022
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Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, and Pascale Fung. 2023 · 2023
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Chatgpt evaluation on sentence level relations: A focus on temporal, causal, and discourse relations
Chunkit Chan, Jiayang Cheng, Weiqi Wang, Yuxin Jiang, Tianqing Fang, Xin Liu, and Yangqiu Song. 2023 · 2023
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Medalpaca–an open-source collection of medical conversational ai models and training data
Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem. 2023 · 2023
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Chatgpt for good? on opportunities and challenges of large language models for education
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Bloomberggpt: A large language model for finance
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Enhancing small medical learners with privacy-preserving contextual prompting
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Controlled text generation with natural language instructions
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Chengyuan Deng, Yiqun Duan, Xin Jin, Heng Chang, Yijun Tian, Han Liu, Henry Peng Zou, Yiqiao Jin, Yijia Xiao, Yichen Wang, et al. 2024 · 2024
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Dp-opt: Make large language model your privacy-preserving prompt engineer
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Multi-agent vqa: Exploring multi-agent foundation models in zero-shot visual question answering
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MUFFIN: Curating multi-faceted instructions for improving instruction following
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In-context time series predictor
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Can LLMs keep a secret? testing privacy implications of language models via contextual integrity theory
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Ceb: Compositional evaluation benchmark for fairness in large language models
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Logicvista: Multimodal llm logical reasoning benchmark in visual contexts
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When search engine services meet large language models: Visions and challenges
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Can llms reason in the wild with programs?
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Prototypical reward network for data-efficient rlhf
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