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20212024
most citedTaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs

9 citations · 23 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.CL2024

Hypertext Entity Extraction in Webpage

Yifei Yang, Tianqiao Liu, Bo Shao +4

Webpage entity extraction is a fundamental natural language processing task in both research and applications. Nowadays, the majority of webpage entity extraction models are traine…

cs.CL20241 cited

Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers

Nuo Chen, Ning Wu, Shining Liang +4

This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing. Instead of a…

cs.CL2023

Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency

Zilin Xiao, Linjun Shou, Xingyao Zhang +4

Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities using…

cs.CL2023

Instructed Language Models with Retrievers Are Powerful Entity Linkers

Zilin Xiao, Ming Gong, Jie Wu +4

Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities. Yet the generative nature stil…

cs.CL2023

Alleviating Over-smoothing for Unsupervised Sentence Representation

Nuo Chen, Linjun Shou, Ming Gong +5

Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language mo…

cs.CL20236 cited

Bridge the Gap between Language models and Tabular Understanding

Nuo Chen, Linjun Shou, Ming Gong +5

Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising finding…