Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Liu, Zhengying"'
Autoformalization aims to convert informal mathematical proofs into machine-verifiable formats, bridging the gap between natural and formal languages. However, ensuring semantic alignment between the informal and formalized statements remains challen
Externí odkaz:
http://arxiv.org/abs/2410.10135
Autor:
Liu, Weiwen, Huang, Xu, Zeng, Xingshan, Hao, Xinlong, Yu, Shuai, Li, Dexun, Wang, Shuai, Gan, Weinan, Liu, Zhengying, Yu, Yuanqing, Wang, Zezhong, Wang, Yuxian, Ning, Wu, Hou, Yutai, Wang, Bin, Wu, Chuhan, Wang, Xinzhi, Liu, Yong, Wang, Yasheng, Tang, Duyu, Tu, Dandan, Shang, Lifeng, Jiang, Xin, Tang, Ruiming, Lian, Defu, Liu, Qun, Chen, Enhong
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. However, real function-calling data is quite challenging to collect and
Externí odkaz:
http://arxiv.org/abs/2409.00920
Autor:
Lin, Xiaohan, Cao, Qingxing, Huang, Yinya, Wang, Haiming, Lu, Jianqiao, Liu, Zhengying, Song, Linqi, Liang, Xiaodan
Formal verification (FV) has witnessed growing significance with current emerging program synthesis by the evolving large language models (LLMs). However, current formal verification mainly resorts to symbolic verifiers or hand-craft rules, resulting
Externí odkaz:
http://arxiv.org/abs/2406.14408
Autor:
Lu, Jianqiao, Wan, Yingjia, Liu, Zhengying, Huang, Yinya, Xiong, Jing, Liu, Chengwu, Shen, Jianhao, Jin, Hui, Zhang, Jipeng, Wang, Haiming, Yang, Zhicheng, Tang, Jing, Guo, Zhijiang
Autoformalization, the conversion of natural language mathematics into formal languages, offers significant potential for advancing mathematical reasoning. However, existing efforts are limited to formal languages with substantial online corpora and
Externí odkaz:
http://arxiv.org/abs/2406.01940
Autor:
Wang, Haiming, Xin, Huajian, Liu, Zhengying, Li, Wenda, Huang, Yinya, Lu, Jianqiao, Yang, Zhicheng, Tang, Jing, Yin, Jian, Li, Zhenguo, Liang, Xiaodan
Recent advances in automated theorem proving leverages language models to explore expanded search spaces by step-by-step proof generation. However, such approaches are usually based on short-sighted heuristics (e.g., log probability or value function
Externí odkaz:
http://arxiv.org/abs/2405.14414
Autor:
Lin, Xiaohan, Cao, Qingxing, Huang, Yinya, Yang, Zhicheng, Liu, Zhengying, Li, Zhenguo, Liang, Xiaodan
Humans can develop new theorems to explore broader and more complex mathematical results. While current generative language models (LMs) have achieved significant improvement in automatically proving theorems, their ability to generate new or reusabl
Externí odkaz:
http://arxiv.org/abs/2405.06677
Autor:
Huang, Yinya, Lin, Xiaohan, Liu, Zhengying, Cao, Qingxing, Xin, Huajian, Wang, Haiming, Li, Zhenguo, Song, Linqi, Liang, Xiaodan
Publikováno v:
ICLR 2024 spotlight
Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict and formal multi-step inference, they are appealing domains for explo
Externí odkaz:
http://arxiv.org/abs/2402.08957
Autor:
Sun, Jiankai, Zheng, Chuanyang, Xie, Enze, Liu, Zhengying, Chu, Ruihang, Qiu, Jianing, Xu, Jiaqi, Ding, Mingyu, Li, Hongyang, Geng, Mengzhe, Wu, Yue, Wang, Wenhai, Chen, Junsong, Yin, Zhangyue, Ren, Xiaozhe, Fu, Jie, He, Junxian, Yuan, Wu, Liu, Qi, Liu, Xihui, Li, Yu, Dong, Hao, Cheng, Yu, Zhang, Ming, Heng, Pheng Ann, Dai, Jifeng, Luo, Ping, Wang, Jingdong, Wen, Ji-Rong, Qiu, Xipeng, Guo, Yike, Xiong, Hui, Liu, Qun, Li, Zhenguo
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial Genera
Externí odkaz:
http://arxiv.org/abs/2312.11562
Autor:
Ji, Yuanfeng, Ge, Chongjian, Kong, Weikai, Xie, Enze, Liu, Zhengying, Li, Zhengguo, Luo, Ping
With the advancements in Large Language Models (LLMs), Vision-Language Models (VLMs) have reached a new level of sophistication, showing notable competence in executing intricate cognition and reasoning tasks. However, existing evaluation benchmarks,
Externí odkaz:
http://arxiv.org/abs/2311.14580
Autor:
Chen, Kai, Wang, Chunwei, Yang, Kuo, Han, Jianhua, Hong, Lanqing, Mi, Fei, Xu, Hang, Liu, Zhengying, Huang, Wenyong, Li, Zhenguo, Yeung, Dit-Yan, Shang, Lifeng, Jiang, Xin, Liu, Qun
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when LLMs inadvertently generate harmful or toxic content, either unintent
Externí odkaz:
http://arxiv.org/abs/2310.10477