Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Feng, Yihao"'
Autor:
Zhang, Jianguo, Lan, Tian, Zhu, Ming, Liu, Zuxin, Hoang, Thai, Kokane, Shirley, Yao, Weiran, Tan, Juntao, Prabhakar, Akshara, Chen, Haolin, Liu, Zhiwei, Feng, Yihao, Awalgaonkar, Tulika, Murthy, Rithesh, Hu, Eric, Chen, Zeyuan, Xu, Ran, Niebles, Juan Carlos, Heinecke, Shelby, Wang, Huan, Savarese, Silvio, Xiong, Caiming
Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality
Externí odkaz:
http://arxiv.org/abs/2409.03215
Autor:
Qin, Can, Xia, Congying, Ramakrishnan, Krithika, Ryoo, Michael, Tu, Lifu, Feng, Yihao, Shu, Manli, Zhou, Honglu, Awadalla, Anas, Wang, Jun, Purushwalkam, Senthil, Xue, Le, Zhou, Yingbo, Wang, Huan, Savarese, Silvio, Niebles, Juan Carlos, Chen, Zeyuan, Xu, Ran, Xiong, Caiming
We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI's Sora, we explore the latent diffusion model (LDM) architecture and i
Externí odkaz:
http://arxiv.org/abs/2408.12590
Autor:
Zhang, Kexun, Yao, Weiran, Liu, Zuxin, Feng, Yihao, Liu, Zhiwei, Murthy, Rithesh, Lan, Tian, Li, Lei, Lou, Renze, Xu, Jiacheng, Pang, Bo, Zhou, Yingbo, Heinecke, Shelby, Savarese, Silvio, Wang, Huan, Xiong, Caiming
Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27% of real GitHub issues in SWE-Bench Lite. However, these sophisticated
Externí odkaz:
http://arxiv.org/abs/2408.07060
The most fundamental capability of modern AI methods such as Large Language Models (LLMs) is the ability to predict the next token in a long sequence of tokens, known as ``sequence modeling." Although the Transformers model is the current dominant ap
Externí odkaz:
http://arxiv.org/abs/2407.14207
Autor:
Liu, Zuxin, Hoang, Thai, Zhang, Jianguo, Zhu, Ming, Lan, Tian, Kokane, Shirley, Tan, Juntao, Yao, Weiran, Liu, Zhiwei, Feng, Yihao, Murthy, Rithesh, Yang, Liangwei, Savarese, Silvio, Niebles, Juan Carlos, Wang, Huan, Heinecke, Shelby, Xiong, Caiming
The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed to synthesize verifiable high-quality datasets for function-calling app
Externí odkaz:
http://arxiv.org/abs/2406.18518
Autor:
Zhang, Ruohong, Gui, Liangke, Sun, Zhiqing, Feng, Yihao, Xu, Keyang, Zhang, Yuanhan, Fu, Di, Li, Chunyuan, Hauptmann, Alexander, Bisk, Yonatan, Yang, Yiming
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However, in tasks involving video instruction-following, providing informative
Externí odkaz:
http://arxiv.org/abs/2404.01258
Autor:
Xia, Congying, Xing, Chen, Du, Jiangshu, Yang, Xinyi, Feng, Yihao, Xu, Ran, Yin, Wenpeng, Xiong, Caiming
This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capability for their application as AI agents. Despite LLMs' advancements, e
Externí odkaz:
http://arxiv.org/abs/2402.18667
Autor:
Zhang, Jianguo, Lan, Tian, Murthy, Rithesh, Liu, Zhiwei, Yao, Weiran, Tan, Juntao, Hoang, Thai, Yang, Liangwei, Feng, Yihao, Liu, Zuxin, Awalgaonkar, Tulika, Niebles, Juan Carlos, Savarese, Silvio, Heinecke, Shelby, Wang, Huan, Xiong, Caiming
Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presents inherent challenges due to the heterogeneous nature of diverse data
Externí odkaz:
http://arxiv.org/abs/2402.15506
Autor:
Wang, Shiyu, Feng, Yihao, Lan, Tian, Yu, Ning, Bai, Yu, Xu, Ran, Wang, Huan, Xiong, Caiming, Savarese, Silvio
Natural language serves as a common and straightforward control signal for humans to interact seamlessly with machines. Recognizing the importance of this interface, the machine learning community is investing considerable effort in generating data t
Externí odkaz:
http://arxiv.org/abs/2402.10941
Autor:
Liu, Zhiwei, Yao, Weiran, Zhang, Jianguo, Xue, Le, Heinecke, Shelby, Murthy, Rithesh, Feng, Yihao, Chen, Zeyuan, Niebles, Juan Carlos, Arpit, Devansh, Xu, Ran, Mui, Phil, Wang, Huan, Xiong, Caiming, Savarese, Silvio
The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core LLM and interact with environments, which facilitates the ability to
Externí odkaz:
http://arxiv.org/abs/2308.05960