Results 81 to 90 of about 60,614 (154)

Recommender Systems in the Era of Large Language Models (LLMs)

IEEE Transactions on Knowledge and Data Engineering
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component of our daily life, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have made significant advancements in enhancing recommender systems by modeling user-item interactions and ...
Zihuai Zhao, Wenqi Fan, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li   +10 more
openaire   +2 more sources

Large Language Models are Effective Table-to-Text Generators, Evaluators, and Feedback Providers

, 2023
Large language models (LLMs) have shown remarkable ability on controllable text generation. However, the potential of LLMs in generating text from structured tables remains largely under-explored.
Cohan, Arman, Nan, Linyong, Si, Shengyun, Tang, Xiangru, Zhang, Haowei, Zhao, Yilun   +5 more
core  

Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs

ACM SIGKDD Explorations Newsletter
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic ...
Zhikai Chen, Haitao Mao, Hang Li 0007, Wei Jin 0009, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu 0031, Jiliang Tang   +10 more
openaire   +2 more sources

Road of Large Language Model: Source, Challenge, and Future Perspectives

Research
Language model (LM), a foundational algorithm in the development of capable artificial intelligence, has been widely explored, achieving remarkable attainment.
Wei Zhao, Xin Yang, Zhihan Lyu, Cai Xu, Ziyu Guan   +4 more
doaj   +1 more source

Large Language Model (LLM)-Enabled Graphs in Dynamic Networking

IEEE Network
Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas ...
Geng Sun 0001, Yixian Wang, Dusit Niyato, Jiacheng Wang 0001, Xinying Wang, H. Vincent Poor, Khaled B. Letaief   +6 more
openaire   +2 more sources

Ce‐LLMs: Status and trends of education‐specific large language models developed in China

Future in Educational Research
The prevalence of AI hallucination in general‐purpose large language models (LLMs) poses significant pedagogical challenges, particularly in terms of content credibility and reliability.
Tao Xie, Yingli Zhou, Jiazhen Yu
doaj   +1 more source

ATILF at NTCIR-18 RadNLP 2024 Shared Task: With less radiology reports, comes less performance [PDF]

We present our results on the main task and subtask of the NTCIR-18 RadNLP 2024 shared task on the English language. We tested to what extent Large Language Models (LLMs) and Pretrained Language Models (PLMs) can identify and classify tumor types and ...
Aman Sinha, Ioana Buhnila
core   +1 more source

Supervised Knowledge Makes Large Language Models Better In-context Learners

Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded their use in real-world language applications.
Bao, Guangsheng, Chen, Weizhu, Wang, Jindong, Wang, Yidong, Xie, Xing, Xu, Ruochen, Yang, Linyi, Ye, Wei, Yu, Zhuohao, Zhang, Shuibai, Zhang, Yue   +10 more
core  

Assessing Translation capabilities of Large Language Models involving English and Indian Languages

, 2023
Generative Large Language Models (LLMs) have achieved remarkable advancements in various NLP tasks. In this work, our aim is to explore the multilingual capabilities of large language models by using machine translation as a task involving English and 22
Bhaskar, Yash, Krishnamurthy, Parameswari, Mujadia, Vandan, Pavani, Penumalla Aditya, Sharma, Dipti Misra, Shravya, Kukkapalli, Urlana, Ashok   +6 more
core  

LABOR-LLM: Language-Based Occupational Representations with Large Language Models

CoRR
This paper builds an empirical model that predicts a worker's next occupation as a function of the worker's occupational history. Because histories are sequences of occupations, the covariate space is high-dimensional, and further, the outcome (the next occupation) is a discrete choice that can take on many values.
Tianyu Du, Ayush Kanodia, Herman Brunborg, Keyon Vafa, Susan Athey   +4 more
openaire   +2 more sources