Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Weng Yixuan"'
Autor:
Weng, Yixuan, Zhu, Minjun, Bao, Guangsheng, Zhang, Hongbo, Wang, Jindong, Zhang, Yue, Yang, Linyi
The automation of scientific discovery has been a long-standing goal within the research community, driven by the potential to accelerate knowledge creation. While significant progress has been made using commercial large language models (LLMs) as re
Externí odkaz:
http://arxiv.org/abs/2411.00816
Large language models encapsulate knowledge and have demonstrated superior performance on various natural language processing tasks. Recent studies have localized this knowledge to specific model parameters, such as the MLP weights in intermediate la
Externí odkaz:
http://arxiv.org/abs/2409.00617
Taxonomies, which organize domain concepts into hierarchical structures, are crucial for building knowledge systems and downstream applications. As domain knowledge evolves, taxonomies need to be continuously updated to include new concepts. Previous
Externí odkaz:
http://arxiv.org/abs/2406.17739
As language models continue to scale in size and capability, they display an array of emerging behaviors, both beneficial and concerning. This heightens the need to control model behaviors. We hope to be able to control the personality traits of lang
Externí odkaz:
http://arxiv.org/abs/2402.10151
Knowledge Editing (KE) for modifying factual knowledge in Large Language Models (LLMs) has been receiving increasing attention. However, existing knowledge editing methods are entity-centric, and it is unclear whether this approach is suitable for a
Externí odkaz:
http://arxiv.org/abs/2311.09053
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase. Currently, the predominantly employed frameworks lack modul
Externí odkaz:
http://arxiv.org/abs/2308.10252
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps. While existing benchmarks are limited to single-chain or single-hop retrieval scenarios. In this paper
Externí odkaz:
http://arxiv.org/abs/2305.14211
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the success of large language models (LLMs) in complex reasoning tasks in variou
Externí odkaz:
http://arxiv.org/abs/2305.05410
Language models' (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To endow LMs with genuine rule comprehension abilities, we propose "Neural
Externí odkaz:
http://arxiv.org/abs/2304.01665
Autor:
Weng, Yixuan, Zhu, Minjun, Xia, Fei, Li, Bin, He, Shizhu, Liu, Shengping, Sun, Bin, Liu, Kang, Zhao, Jun
Recently, with the chain of thought (CoT) prompting, large language models (LLMs), e.g., GPT-3, have shown strong reasoning ability in several natural language processing tasks such as arithmetic, commonsense, and logical reasoning. However, LLMs wit
Externí odkaz:
http://arxiv.org/abs/2212.09561