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Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench, have become popular for evaluating language models due to their cost-effectiveness and scalability compared to human evaluation.
The ladder: A reliable leaderboard for machine learning competitions
Avrim Blum and Moritz Hardt · 2015
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang · 2018
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HotFlip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2018
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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 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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Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving · 2022
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Evaluating correctness and faithfulness of instruction-following models for question answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy · 2023
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Detecting language model attacks with perplexity
Gabriel Alon and Michael Kamfonas · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
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Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, and Ludwig Schmidt · 2023
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
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Lm vs lm: Detecting factual errors via cross examination
Roi Cohen, May Hamri, Mor Geva, and Amir Globerson · 2023
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Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
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Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing · 2023
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Alpacafarm: A simulation framework for methods that learn from human feedback, 2023
Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Badllama: cheaply removing safety fine-tuning from llama 2-chat 13b
Pranav Gade, Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish · 2023
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Human-like summarization evaluation with chatgpt
Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, and Xiaojun Wan · 2023
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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
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The false promise of imitating proprietary llms
Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song · 2023
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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 · 2023
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Automatically auditing large language models via discrete optimization
Erik Jones, Anca Dragan, Aditi Raghunathan, and Jacob Steinhardt · 2023
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Prometheus: Inducing fine-grained evaluation capability in language models
Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, et al · 2023
Cited alongside, same era.
Open sesame! universal black box jailbreaking of large language models
Raz Lapid, Ron Langberg, and Moshe Sipper · 2023
Cited alongside, same era.
Lora fine-tuning efficiently undoes safety training in llama 2-chat 70b
Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish · 2023
Cited alongside, same era.
Chatgpt as a factual inconsistency evaluator for text summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou · 2023
Cited alongside, same era.
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark JF Gales · 2023
Cited alongside, same era.
Black box adversarial prompting for foundation models
Jailbreaking leading safety-aligned llms with simple adaptive attacks
Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion · 2024
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Many-shot jailbreaking
Cem Anil, Esin Durmus, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Nina Rimsky, Meg Tong, Jesse Mu, Daniel Ford, et al · 2024
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Chatbot arena: An open platform for evaluating llms by human preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E Gonzalez, et al · 2024
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Length-controlled alpacaeval: A simple way to debias automatic evaluators
Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B Hashimoto · 2024
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Query-based adversarial prompt generation
Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, and Milad Nasr · 2024
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Catastrophic jailbreak of open-source llms via exploiting generation
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Natalie Maus, Patrick Chao, Eric Wong, and Jacob Gardner · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2023
Cited alongside, same era.
Tricking llms into disobedience: Understanding, analyzing, and preventing jailbreaks
Abhinav Rao, Sachin Vashistha, Atharva Naik, Somak Aditya, and Monojit Choudhury · 2023
Cited alongside, same era.
Smoothllm: Defending large language models against jailbreaking attacks
Alexander Robey, Eric Wong, Hamed Hassani, and George J Pappas · 2023
Cited alongside, same era.
Identifying the risks of lm agents with an lm-emulated sandbox
Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J Maddison, and Tatsunori Hashimoto · 2023
Cited alongside, same era.
Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou · 2023
Cited alongside, same era.
Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen · 2024
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Prometheus 2: An open source language model specialized in evaluating other language models
Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, and Minjoon Seo · 2024
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Does style matter? disentangling style and substance in chatbot arena, 2024a
Tianle Li, Anastasios Angelopoulos, and Wei-Lin Chiang · 2024
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From live data to high-quality benchmarks: The arena-hard pipeline, april 2024
Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E Gonzalez, and Ion Stoica · 2024
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Amplegcg: Learning a universal and transferable generative model of adversarial suffixes for jailbreaking both open and closed llms
Zeyi Liao and Huan Sun · 2024
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Wildbench: Benchmarking llms with challenging tasks from real users in the wild, 2024
Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, Faeze Brahman, Abhilasha Ravichander, Valentina Pyatkin, Nouha Dziri, Ronan Le Bras, and Yejin Choi · 2024
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Aligning with human judgement: The role of pairwise preference in large language model evaluators
Yinhong Liu, Han Zhou, Zhijiang Guo, Ehsan Shareghi, Ivan Vulic, Anna Korhonen, and Nigel Collier · 2024
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Llm critics help catch llm bugs
Nat McAleese, Rai Michael Pokorny, Juan Felipe Ceron Uribe, Evgenia Nitishinskaya, Maja Trebacz, and Jan Leike · 2024
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Simpo: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
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Llama 3 model card, 2024
Meta · 2024
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Mixeval: Deriving wisdom of the crowd from llm benchmark mixtures
Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, and Yang You · 2024
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Advprompter: Fast adaptive adversarial prompting for llms
Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, and Yuandong Tian · 2024
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Is llm-as-a-judge robust? investigating universal adversarial attacks on zero-shot llm assessment
Vyas Raina, Adian Liusie, and Mark Gales · 2024
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Optimization-based prompt injection attack to llm-as-a-judge
Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, and Neil Zhenqiang Gong · 2024
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How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge ai safety by humanizing llms
Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi · 2024
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From lists to emojis: How format bias affects model alignment
Xuanchang Zhang, Wei Xiong, Lichang Chen, Tianyi Zhou, Heng Huang, and Tong Zhang · 2024
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Long is more for alignment: A simple but tough-to-beat baseline for instruction fine-tuning
Hao Zhao, Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion · 2024
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Improved few-shot jailbreaking can circumvent aligned language models and their defenses
Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Jing Jiang, and Min Lin · 2024
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