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We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM).
A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Content or context moderation?, Nov 2018
Robyn Caplan · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
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The burden of stigma on health and well-being: A taxonomy of concealment, course, disruptiveness, aesthetics, origin, and peril across 93 stigmas
John E. Pachankis, Mark L. Hatzenbuehler, Katie Wang, Charles L. Burton, Forrest W. Crawford, Jo C. Phelan, and Bruce G. Link · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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PAWS: Paraphrase adversaries from word scrambling
Yuan Zhang, Jason Baldridge, and Luheng He · 2019
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald · 2020
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Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis · 2020
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Evaluating groundedness in dialogue systems: The begin benchmark, 2021
Nouha Dziri, Hannah Rashkin, Tal Linzen, and David Reitter · 2021
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Latent hatred: A benchmark for understanding implicit hate speech
Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, and Diyi Yang · 2021
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Summeval: Re-evaluating summarization evaluation
Alexander R Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev · 2021
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Dialfact: A benchmark for fact-checking in dialogue
Prakhar Gupta, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong · 2021
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q 2 q^{2} : Evaluating factual consistency in knowledge-grounded dialogues via question generation and question answering
Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, and Omri Abend · 2021
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Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov · 2021
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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, 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 B. Brown, Jack Clark, Sam McCandlish, Chris Olah, Benjamin Mann, and Jared Kaplan · 2022
Cited alongside, same era.
True: Re-evaluating factual consistency evaluation
Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias · 2022
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Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing
Pengcheng He, Jianfeng Gao, and Weizhu Chen · 2023
Cited alongside, same era.
Detectors for safe and reliable llms: Implementations, uses, and limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre L. Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspoon, and Marcel Zalmanovici · 2024
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Evaluations of machine learning privacy defenses are misleading
Michael Aerni, Jie Zhang, and Florian Tramèr · 2024
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Safe RLHF: safe reinforcement learning from human feedback
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang · 2024
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Aegis: Online adaptive ai content safety moderation with ensemble of llm experts
Shaona Ghosh, Prasoon Varshney, Erick Galinkin, and Christopher Parisien · 2024
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Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa · 2023
Cited alongside, same era.
Beavertails: Towards improved safety alignment of LLM via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2023
Cited alongside, same era.
Toxicchat: Unveiling hidden challenges of toxicity detection in real-world user-ai conversation
Zi Lin, Zihan Wang, Yongqi Tong, Yangkun Wang, Yuxin Guo, Yujia Wang, and Jingbo Shang · 2023
Cited alongside, same era.
A holistic approach to undesired content detection in the real world
Todor Markov, Chong Zhang, Sandhini Agarwal, Florentine Eloundou Nekoul, Theodore Lee, Steven Adler, Angela Jiang, and Lilian Weng · 2023
Cited alongside, same era.
Tree of attacks: Jailbreaking black-box llms automatically, 2023
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi · 2023
Cited alongside, same era.
Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, and Jordan L. Boyd-Graber · 2023
Cited alongside, same era.
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang · 2023
Cited alongside, same era.
Safety assessment of chinese large language models
Hao Sun, Zhexin Zhang, Jiawen Deng, Jiale Cheng, and Minlie Huang · 2023
Cited alongside, same era.
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Granite 3.0 language models, 2024
IBM Granite Team · 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
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Wildteaming at scale: From in-the-wild jailbreaks to (adversarially) safer language models
Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Yejin Choi, and Nouha Dziri · 2024
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Harmbench: A standardized evaluation framework for automated red teaming and robust refusal
Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David Forsyth, and Dan Hendrycks · 2024
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AI safety v0.5 proof of concept
MLCommons · 2024
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SocialStigmaQA: A benchmark to uncover stigma amplification in generative language models
Manish Nagireddy, Lamogha Chiazor, Moninder Singh, and Ioana Baldini · 2024
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OWASP Top 10 for Large Language Model Applications
OWASP · 2024
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Attack atlas: A practitioner’s perspective on challenges and pitfalls in red teaming genai, 2024
Ambrish Rawat, Stefan Schoepf, Giulio Zizzo, Giandomenico Cornacchia, Muhammad Zaid Hameed, Kieran Fraser, Erik Miehling, Beat Buesser, Elizabeth M. Daly, Mark Purcell, Prasanna Sattigeri, Pin-Yu Chen, and Kush R. Varshney · 2024
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Xstest: A test suite for identifying exaggerated safety behaviours in large language models
Paul Röttger, Hannah Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy · 2024
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Peter Slattery, Alexander K. Saeri, Emily A. C. Grundy, Jess Graham, Michael Noetel, Risto Uuk, James Dao, Soroush Pour, Stephen Casper, and Neil Thompson · 2024
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MiniCheck: Efficient fact-checking of LLMs on grounding documents
Liyan Tang, Philippe Laban, and Greg Durrett · 2024
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Do-not-answer: Evaluating safeguards in llms
Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, and Timothy Baldwin · 2024
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Shieldgemma: Generative AI content moderation based on gemma
Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, and Oscar Wahltinez · 2024
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