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Large Language Models (LLMs) have emerged as a dominant approach for a wide range of NLP tasks, with their access to external information further enhancing their capabilities.
Hierarchical mixtures of experts and the em algorithm
M.I. Jordan and R.A. Jacobs. 1993 · 1993
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2005
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Acl 2019 fourth conference on machine translation (wmt19), shared task: Machine translation of news
Wikimedia Foundation · 2019
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SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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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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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Ouyang Long, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2021 · 2021
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Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby. 2021 · 2021
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Switch transformers: scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2022 · 2022
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Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro. 2022 · 2022
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Language models are multilingual chain-of-thought reasoners
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, and Jason Wei. 2022 · 2022
Cited alongside, same era.
Gpt-4 technical report
Josh Achiam, Steven Adler, and Barret Zoph. 2023 · 2023
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Detecting language model attacks with perplexity
Gabriel Alon and Michael J Kamfonas. 2024 · 2024
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Multi-expert prompting improves reliability, safety and usefulness of large language models
Xuan Long Do, Ngoc Yen Duong, Anh Tuan Luu, Kenji Kawaguchi, Min-Yen Kan, and Nancy F. Chen. 2024 · 2024
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Abhimanyu Dubey, Abhinav Jauhri, and Zhiwei Zhao. 2024 · 2024
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Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms
Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, and Nouha Dziri. 2024 · 2024
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Defending against indirect prompt injection attacks with spotlighting
Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, and Emre Kiciman. 2024 · 2024
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Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023 · 2023
Cited alongside, same era.
Promptboosting: black-box text classification with ten forward passes
Bairu Hou, Joe O’Connor, Jacob Andreas, Shiyu Chang, and Yang Zhang. 2023 · 2023
Cited alongside, same era.
Token-level adversarial prompt detection based on perplexity measures and contextual information
Zhengmian Hu, Gang Wu, Saayan Mitra, Ruiyi Zhang, Tong Sun, Heng Huang, and Vishy Swaminathan. 2023 · 2023
Cited alongside, same era.
Openai evals
OpenAI. 2023 · 2023
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Boosted prompt ensembles for large language models
Silviu Pitis, Michael Ruogu Zhang, Andrew Wang, and Jimmy Ba. 2023 · 2023
Cited alongside, same era.
Jailbroken: How does LLM safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023 · 2023
Cited alongside, same era.
Benchmarking and defending against indirect prompt injection attacks on large language models
Jingwei Yi, Yueqi Xie, Bin Zhu, Keegan Hines, Emre Kiciman, Guangzhong Sun, Xing Xie, and Fangzhao Wu. 2023 · 2023
Cited alongside, same era.
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Baseline defenses for adversarial attacks against aligned language models
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein. 2024 · 2024
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Qwen2.5: A party of foundation models
Qwen. 2024 · 2024
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Tensor trust: Interpretable prompt injection attacks from an online game
Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, and Stuart Russell. 2024 · 2024
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The instruction hierarchy: Training llms to prioritize privileged instructions
Eric Wallace, Kai Xiao, Reimar H. Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. 2024 · 2024
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Defending llms against jailbreaking attacks via backtranslation
Yihan Wang, Zhouxing Shi, Andrew Bai, and Cho-Jui Hsieh. 2024 · 2024
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GPT-4 is too smart to be safe: Stealthy chat with LLMs via cipher
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen tse Huang, Pinjia He, Shuming Shi, and Zhaopeng Tu. 2024 · 2024
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Prefer: Prompt ensemble learning via feedback-reflect-refine
Chenrui Zhang, Lin Liu, Chuyuan Wang, Xiao Sun, Hongyu Wang, Jinpeng Wang, and Mingchen Cai. 2024 · 2024
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