Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Wu, Tongtong"'
Autor:
Zhao, Weixiang, Hu, Yulin, Guo, Jiahe, Sui, Xingyu, Wu, Tongtong, Deng, Yang, Zhao, Yanyan, Qin, Bing, Che, Wanxiang, Liu, Ting
Despite the growing global demand for large language models (LLMs) that serve users from diverse linguistic backgrounds, most cutting-edge LLMs remain predominantly English-centric. This creates a performance gap across languages, restricting access
Externí odkaz:
http://arxiv.org/abs/2410.04407
Autor:
Wang, Yu, Han, Chi, Wu, Tongtong, He, Xiaoxin, Zhou, Wangchunshu, Sadeq, Nafis, Chen, Xiusi, He, Zexue, Wang, Wei, Haffari, Gholamreza, Ji, Heng, McAuley, Julian
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central to this is enabling continuous, high-frequency interactions, where the
Externí odkaz:
http://arxiv.org/abs/2409.13265
Autor:
Wu, Tongtong, Wu, Weigang, Wang, Xingyu, Xu, Kang, Ma, Suyu, Jiang, Bo, Yang, Ping, Xing, Zhenchang, Li, Yuan-Fang, Haffari, Gholamreza
Significant research has focused on improving the performance of large language model on code-related tasks due to their practical importance. Although performance is typically evaluated using public benchmark datasets, the existing datasets do not a
Externí odkaz:
http://arxiv.org/abs/2406.07411
Autor:
Kang, Jingqi, Wu, Tongtong, Zhao, Jinming, Wang, Guitao, Wei, Yinwei, Yang, Hao, Qi, Guilin, Li, Yuan-Fang, Haffari, Gholamreza
Speech event detection is crucial for multimedia retrieval, involving the tagging of both semantic and acoustic events. Traditional ASR systems often overlook the interplay between these events, focusing solely on content, even though the interpretat
Externí odkaz:
http://arxiv.org/abs/2404.13289
As the parameter scale of large language models (LLMs) grows, jointly training knowledge graph (KG) embeddings with model parameters to enhance LLM capabilities becomes increasingly costly. Consequently, the community has shown interest in developing
Externí odkaz:
http://arxiv.org/abs/2402.11541
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale. However, updates are necessary to endow LLMs with new skills and keep them up-to-date with rapidly evolving human know
Externí odkaz:
http://arxiv.org/abs/2402.01364
Autor:
Kang, Jingqi, Wu, Tongtong, Zhao, Jinming, Wang, Guitao, Qi, Guilin, Li, Yuan-Fang, Haffari, Gholamreza
While text-based event extraction has been an active research area and has seen successful application in many domains, extracting semantic events from speech directly is an under-explored problem. In this paper, we introduce the Speech Event Extract
Externí odkaz:
http://arxiv.org/abs/2401.15385
The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention. The current methods for automatically evaluating the attribution, which are often based on Large Language Mo
Externí odkaz:
http://arxiv.org/abs/2401.14640
Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning process for SGG. This conventional paradigm may necessitate repetitive tra
Externí odkaz:
http://arxiv.org/abs/2401.14626
Norms, which are culturally accepted guidelines for behaviours, can be integrated into conversational models to generate utterances that are appropriate for the socio-cultural context. Existing methods for norm recognition tend to focus only on surfa
Externí odkaz:
http://arxiv.org/abs/2305.16598