Zobrazeno 1 - 10
of 234
pro vyhledávání: '"Xu Frank"'
Autor:
Xu, Frank F., Song, Yufan, Li, Boxuan, Tang, Yuxuan, Jain, Kritanjali, Bao, Mengxue, Wang, Zora Z., Zhou, Xuhui, Guo, Zhitong, Cao, Murong, Yang, Mingyang, Lu, Hao Yang, Martin, Amaad, Su, Zhe, Maben, Leander, Mehta, Raj, Chi, Wayne, Jang, Lawrence, Xie, Yiqing, Zhou, Shuyan, Neubig, Graham
We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there
Externí odkaz:
http://arxiv.org/abs/2412.14161
Autor:
De Chezelles, Thibault Le Sellier, Gasse, Maxime, Drouin, Alexandre, Caccia, Massimo, Boisvert, Léo, Thakkar, Megh, Marty, Tom, Assouel, Rim, Shayegan, Sahar Omidi, Jang, Lawrence Keunho, Lù, Xing Han, Yoran, Ori, Kong, Dehan, Xu, Frank F., Reddy, Siva, Cappart, Quentin, Neubig, Graham, Salakhutdinov, Ruslan, Chapados, Nicolas, Lacoste, Alexandre
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fra
Externí odkaz:
http://arxiv.org/abs/2412.05467
Web browsers are a portal to the internet, where much of human activity is undertaken. Thus, there has been significant research work in AI agents that interact with the internet through web browsing. However, there is also another interface designed
Externí odkaz:
http://arxiv.org/abs/2410.16464
Autor:
Zhou, Xuhui, Kim, Hyunwoo, Brahman, Faeze, Jiang, Liwei, Zhu, Hao, Lu, Ximing, Xu, Frank, Lin, Bill Yuchen, Choi, Yejin, Mireshghallah, Niloofar, Bras, Ronan Le, Sap, Maarten
AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examining AI agent safety within diverse and complex social interactions. HAI
Externí odkaz:
http://arxiv.org/abs/2409.16427
Autor:
Ou, Tianyue, Xu, Frank F., Madaan, Aman, Liu, Jiarui, Lo, Robert, Sridhar, Abishek, Sengupta, Sudipta, Roth, Dan, Neubig, Graham, Zhou, Shuyan
LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives (e.g., arranging an online meeting). However, accuracy is still far from satisfactory, partly due to a lack of large-scale, direct demonstr
Externí odkaz:
http://arxiv.org/abs/2409.15637
Autor:
Monokroussos Christos, Zhang Yating, Lee Eleanor W., Xu Frank, Zhou Allen, Zhang Yichi, Herrmann Werner
Publikováno v:
EPJ Photovoltaics, Vol 14, p 6 (2023)
As energy yields of photovoltaic modules are highly related to local climate and ambient conditions, it is necessary to assess the energy-yield performance of PV modules under various operating conditions. This work compares commercial crystalline si
Externí odkaz:
https://doaj.org/article/8a3a824ef9ce4dd7990e8f2a6e51db33
Autor:
Wang, Xingyao, Li, Boxuan, Song, Yufan, Xu, Frank F., Tang, Xiangru, Zhuge, Mingchen, Pan, Jiayi, Song, Yueqi, Li, Bowen, Singh, Jaskirat, Tran, Hoang H., Li, Fuqiang, Ma, Ren, Zheng, Mingzhang, Qian, Bill, Shao, Yanjun, Muennighoff, Niklas, Zhang, Yizhe, Hui, Binyuan, Lin, Junyang, Brennan, Robert, Peng, Hao, Ji, Heng, Neubig, Graham
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there ha
Externí odkaz:
http://arxiv.org/abs/2407.16741
Autor:
Wang, Zora Zhiruo, Asai, Akari, Yu, Xinyan Velocity, Xu, Frank F., Xie, Yiqing, Neubig, Graham, Fried, Daniel
While language models (LMs) have proven remarkably adept at generating code, many programs are challenging for LMs to generate using their parametric knowledge alone. Providing external contexts such as library documentation can facilitate generating
Externí odkaz:
http://arxiv.org/abs/2406.14497
Autor:
Zhou, Shuyan, Xu, Frank F., Zhu, Hao, Zhou, Xuhui, Lo, Robert, Sridhar, Abishek, Cheng, Xianyi, Ou, Tianyue, Bisk, Yonatan, Fried, Daniel, Alon, Uri, Neubig, Graham
With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect w
Externí odkaz:
http://arxiv.org/abs/2307.13854
Large language models (LLMs) struggle on processing complicated observations in interactive decision making tasks. To alleviate this issue, we propose a simple hierarchical prompting approach. Diverging from previous prompting approaches that always
Externí odkaz:
http://arxiv.org/abs/2305.14257