Zobrazeno 1 - 10
of 5 830
pro vyhledávání: '"Chengjin An"'
Autor:
Gu, Jiawei, Jiang, Xuhui, Shi, Zhichao, Tan, Hexiang, Zhai, Xuehao, Xu, Chengjin, Li, Wei, Shen, Yinghan, Ma, Shengjie, Liu, Honghao, Wang, Yuanzhuo, Guo, Jian
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across div
Externí odkaz:
http://arxiv.org/abs/2411.15594
Autor:
Li, Muzhi, Yang, Cehao, Xu, Chengjin, Jiang, Xuhui, Qi, Yiyan, Guo, Jian, Leung, Ho-fung, King, Irwin
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. On the
Externí odkaz:
http://arxiv.org/abs/2411.08165
Autor:
Wu, Xiaojun, Liu, Junxi, Su, Huanyi, Lin, Zhouchi, Qi, Yiyan, Xu, Chengjin, Su, Jiajun, Zhong, Jiajie, Wang, Fuwei, Wang, Saizhuo, Hua, Fengrui, Li, Jia, Guo, Jian
As large language models become increasingly prevalent in the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. However, existing finance benchmarks often suffer from limited language an
Externí odkaz:
http://arxiv.org/abs/2411.06272
Autor:
Li, Muzhi, Yang, Cehao, Xu, Chengjin, Song, Zixing, Jiang, Xuhui, Guo, Jian, Leung, Ho-fung, King, Irwin
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, these methods depend heavily on t
Externí odkaz:
http://arxiv.org/abs/2410.16803
Retrieval-augmented generation (RAG) framework has shown promising potential in knowledge-intensive question answering (QA) by retrieving external corpus and generating based on augmented context. However, existing approaches only consider the query
Externí odkaz:
http://arxiv.org/abs/2410.22353
Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in chart understanding through domain-specific alignment and fine-tuning. Howe
Externí odkaz:
http://arxiv.org/abs/2409.03277
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in processing and generating content across multiple data modalities. However, a significant drawback of MLLMs is their reliance on static training data, leading to ou
Externí odkaz:
http://arxiv.org/abs/2407.21439
Autor:
Ma, Shengjie, Xu, Chengjin, Jiang, Xuhui, Li, Muzhi, Qu, Huaren, Yang, Cehao, Mao, Jiaxin, Guo, Jian
Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often fall short of ensuring the depth and completeness of retrieved informat
Externí odkaz:
http://arxiv.org/abs/2407.10805
Artificial intelligence is making significant strides in the finance industry, revolutionizing how data is processed and interpreted. Among these technologies, large language models (LLMs) have demonstrated substantial potential to transform financia
Externí odkaz:
http://arxiv.org/abs/2407.00365
Knowledge Graphs (KGs) are foundational structures in many AI applications, representing entities and their interrelations through triples. However, triple-based KGs lack the contextual information of relational knowledge, like temporal dynamics and
Externí odkaz:
http://arxiv.org/abs/2406.11160