◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

E. Cambria

7 papers hereh-index 11557.2k citations502 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author5

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV2
  • cs.AI1

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Large Language Models for Few-Shot Named Entity Recognition

Yufei Zhao, Xiaoshi Zhong, Erik Cambria +1

Named entity recognition (NER) is a fundamental task in numerous downstream applications. Recently, researchers have employed pre-trained language models (PLMs) and large language…

cs.CL2024

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

Fangzhi Xu, Qika Lin, Jiawei Han +3

Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs)…

cs.CL2024

Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation

Mohammad Nadeem, Shahab Saquib Sohail, Erik Cambria +2

Foundational Large Language Models (LLMs) have changed the way we perceive technology. They have been shown to excel in tasks ranging from poem writing and coding to essay generati…

cs.CL2024

Large Language Models for Automated Open-domain Scientific Hypotheses Discovery

Zonglin Yang, Xinya Du, Junxian Li +3

Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations. Past…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.