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Through alignment with human preferences, Large Language Models (LLMs) have advanced significantly in generating honest, harmless, and helpful responses.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 1908
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Xnli: Evaluating cross-lingual sentence representations
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Can a suit of armor conduct electricity? a new dataset for open book question answering
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Mathqa: Towards interpretable math word problem solving with operation-based formalisms, 2019
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Hellaswag: Can a machine really finish your sentence?
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PROST: Physical reasoning about objects through space and time
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Benjamin Mann, Nova Dassarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, John Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Christopher Olah, and Jared Kaplan · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, 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
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
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Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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Toxigen: A large-scale machine-generated dataset for implicit and adversarial hate speech detection
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar · 2022
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Unnatural instructions: Tuning language models with (almost) no human labor
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick · 2022
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Explaining patterns in data with language models via interpretable autoprompting
Chandan Singh, John Xavier Morris, Jyoti Aneja, Alexander M Rush, and Jianfeng Gao · 2022
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Describing differences between text distributions with natural language
Ruiqi Zhong, Charlie Snell, Dan Klein, and Jacob Steinhardt · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Open llm leaderboard
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Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Ultrafeedback: Boosting language models with high-quality feedback, 2023
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
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Enhancing chat language models by scaling high-quality instructional conversations, 2023
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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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Kto: Model alignment as prospect theoretic optimization
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A framework for few-shot language model evaluation, 07 2024
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Towards comprehensive preference data collection for reward modeling
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Reinforced self-training (rest) for language modeling
Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alexa Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, A. Doucet, Orhan Firat, and Nando de Freitas · 2023
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Won’t get fooled again: Answering questions with false premises
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Llm-blender: Ensembling large language models with pairwise ranking and generative fusion
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Textbooks are all you need ii: phi-1.5 technical report
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Self: Self-evolution with language feedback
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Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
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Self-refine: Iterative refinement with self-feedback
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Shengyi Costa Huang, Agustín Piqueres, Kashif Rasul, Philipp Schmid, Daniel Vila, and Lewis Tunstall · 2024
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Aligning large language models with self-generated preference data
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Arena learning: Build data flywheel for llms post-training via simulated chatbot arena
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Simpo: Simple preference optimization with a reference-free reward
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Better alignment with instruction back-and-forth translation
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Direct preference optimization: Your language model is secretly a reward model
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Direct nash optimization: Teaching language models to self-improve with general preferences
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Scaling data diversity for fine-tuning language models in human alignment
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Toward self-improvement of llms via imagination, searching, and criticizing
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Self-taught evaluators
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Self-augmented preference optimization: Off-policy paradigms for language model alignment
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Self-rewarding language models
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Rest-mcts*: Llm self-training via process reward guided tree search
Dan Zhang, Sining Zhoubian, Yisong Yue, Yuxiao Dong, and Jie Tang · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
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