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Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers.
Overview of the fourth message understanding evaluation and conference
Beth Sundheim · 1992
Earlier work this paper cites.
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
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Chin-Yew Lin · 2004
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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VQA: visual question answering
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
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Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi · 2020
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Training verifiers to solve math word problems
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Chatgpt blog post
OpenAI · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
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CERT: continual pre-training on sketches for library-oriented code generation
Daoguang Zan, Bei Chen, Dejian Yang, Zeqi Lin, Minsu Kim, Bei Guan, Yongji Wang, Weizhu Chen, and Jian-Guang Lou · 2022
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Mind2web: Towards a generalist agent for the web
Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samual Stevens, Boshi Wang, Huan Sun, and Yu Su · 2023
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PAL: program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Don’t generate, discriminate: A proposal for grounding language models to real-world environments
Yu Gu, Xiang Deng, and Yu Su · 2023
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Benchmarking large language models as AI research agents
Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec · 2023
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Fangkai Jiao, Bosheng Ding, Tianze Luo, and Zhanfeng Mo · 2023
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Swe-bench: Can language models resolve real-world github issues?
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
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jupyter-ai
jupyter-ai team · 2023
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A unified hallucination mitigation framework for large vision-language models
Yue Chang, Liqiang Jing, Xiaopeng Zhang, and Yue Zhang · 2024
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Workarena: How capable are web agents at solving common knowledge work tasks?
Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vázquez, Nicolas Chapados, and Alexandre Lacoste · 2024
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Ds-agent: Automated data science by empowering large language models with case-based reasoning
Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, and Jun Wang · 2024
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Webvoyager: Building an end-to-end web agent with large multimodal models
Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu · 2024
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DS-1000: A natural and reliable benchmark for data science code generation
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Sheetcopilot: Bringing software productivity to the next level through large language models
Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, and Zhaoxiang Zhang · 2023
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan · 2023
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GPQA: A graduate-level google-proof q&a benchmark
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Reflexion: an autonomous agent with dynamic memory and self-reflection
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Dual consistency-enhanced semi-supervised sentiment analysis towards COVID-19 tweets
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Xagent: An autonomous agent for complex task solving, 2023
XAgent Team · 2023
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Wenyi Wang, Xiangru Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Zongze Xu, and Chenglin Wu · 2024
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Coderag-bench: Can retrieval augment code generation?
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Os-copilot: Towards generalist computer agents with self-improvement
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Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments
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Swe-agent: Agent-computer interfaces enable automated software engineering
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, Thong Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, David Lo, Binyuan Hui, Niklas Muennighoff, Daniel Fried, Xiaoning Du, Harm de Vries, and Leandro von Werra · 2024
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