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Recent advancements in LLMs have shown their significant potential in tasks like text summarization and generation.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. 2022 · 1965
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
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, et al · 2021
Earlier work this paper cites.
Github copilot in the classroom: learning to code with AI assistance
Ben Puryear and Gina Sprint. 2022 · 2022
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Gpt-4 technical report
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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Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023 · 2023
Earlier work this paper cites.
Investigating the potential of gpt-3 in providing feedback for programming assessments
Rishabh Balse, Bharath Valaboju, Shreya Singhal, Jayakrishnan Madathil Warriem, and Prajish Prasad. 2023 · 2023
Earlier work this paper cites.
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 · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
Earlier work this paper cites.
Advancements in Scientific Controllable Text Generation Methods
Arnav Goel, Medha Hira, Avinash Anand, Siddhesh Bangar, and Dr Rajiv Ratn Shah. 2023 · 2023
Cited alongside, same era.
Decomposed Prompting to Answer Questions on a Course Discussion Board
Brandon Jaipersaud, Paul Zhang, Jimmy Ba, Andrew Petersen, Lisa Zhang, and Michael R Zhang. 2023 · 2023
Cited alongside, same era.
Understanding the effects of rlhf on llm generalisation and diversity
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. 2023 · 2023
Cited alongside, same era.
Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi. 2023 · 2023
Cited alongside, same era.
Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges
Qingyao Li, Lingyue Fu, Weiming Zhang, Xianyu Chen, Jingwei Yu, Wei Xia, Weinan Zhang, Ruiming Tang, and Yong Yu. 2023 · 2023
Cited alongside, same era.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
Later among the works it cites.
Let the llms talk: Simulating human-to-human conversational qa via zero-shot llm-to-llm interactions
Zahra Abbasiantaeb, Yifei Yuan, Evangelos Kanoulas, and Mohammad Aliannejadi. 2024 · 2024
Closest in time.
MM-PhyQA: Multimodal Physics Question-Answering With Multi-Image CoT Prompting
Avinash Anand, Janak Kapuriya, Apoorv Singh, Jay Saraf, Naman Lal, Astha Verma, Rushali Gupta, and Rajiv Shah. 2024 · 2024
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Future applications of generative large language models: A data-driven case study on ChatGPT
Chiarello Filippo, Giordano Vito, Spada Irene, Barandoni Simone, and Fantoni Gualtiero. 2024 · 2024
Closest in time.
Elevating Learning Experiences: Leveraging Large Language Models as Student-Facing Assistants in Discussion Forums
Chancharik Mitra, Mihran Miroyan, Rishi Jain, Vedant Kumud, Gireeja Ranade, and Narges Norouzi. 2024 · 2024
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Large Language Models (GPT) for automating feedback on programming assignments
Maciej Pankiewicz and Ryan S Baker. 2023 · 2023
Cited alongside, same era.
Kosmos-2: Grounding multimodal large language models to the world
Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. 2023 · 2023
Cited alongside, same era.
The robots are here: Navigating the generative ai revolution in computing education
James Prather, Paul Denny, Juho Leinonen, Brett A Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton-Reilly, et al · 2023
Cited alongside, same era.
Image captioning for effective use of language models in knowledge-based visual question answering
Ander Salaberria, Gorka Azkune, Oier Lopez de Lacalle, Aitor Soroa, and Eneko Agirre. 2023 · 2023
Cited alongside, same era.
Evaluating reading comprehension exercises generated by LLMs: A showcase of ChatGPT in education applications
Changrong Xiao, Sean Xin Xu, Kunpeng Zhang, Yufang Wang, and Lei Xia. 2023 · 2023
Cited alongside, same era.
Olagpt: Empowering llms with human-like problem-solving abilities
Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei, Chenglin Li, Beibei Kong, Lei Chen, Chengxiang Zhuo, Bo Hu, and Zang Li. 2023 · 2023
Cited alongside, same era.
Revolutionizing High School Physics Education: A Novel Dataset
Avinash Anand, Krishnasai Addala, Kabir Baghel, Arnav Goel, Medha Hira, Rushali Gupta, and Rajiv Ratn Shah. 2023a
Cited in the paper.
Closest in time.
Embodiedgpt: Vision-language pre-training via embodied chain of thought
Yao Mu, Qinglong Zhang, Mengkang Hu, Wenhai Wang, Mingyu Ding, Jun Jin, Bin Wang, Jifeng Dai, Yu Qiao, and Ping Luo. 2024 · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Assessing concepts, procedures, and cognitive demand of ChatGPT-generated mathematical tasks
Bima Sapkota and Liza Bondurant. 2024 · 2024
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Visionllm: Large language model is also an open-ended decoder for vision-centric tasks
Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie Zhou, Yu Qiao, et al · 2024
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MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, et al · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
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