专题:Text Readability and Simplification

This cluster of papers focuses on automatic text simplification and readability assessment using machine learning, statistical language models, neural networks, and natural language processing techniques. The research covers areas such as sentence simplification, lexical simplification, complex word identification, and semantic simplification to improve the accessibility and comprehension of written text.
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近5年高被引文献
Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics

article Full Text OpenAlex 14304 FWCI1476.4518

BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence

preprint Full Text OpenAlex 4301 FWCI0

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

article Full Text OpenAlex 1865 FWCI429.3313

Scaling Instruction-Finetuned Language Models

preprint Full Text OpenAlex 1198 FWCI0

Large Language Models are Zero-Shot Reasoners

preprint Full Text OpenAlex 1114 FWCI0

Prompt Engineering with ChatGPT: A Guide for Academic Writers

article Full Text OpenAlex 748 FWCI95.6675

G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment

conference-paper Full Text OpenAlex 686 FWCI143.9218

UniXcoder: Unified Cross-Modal Pre-training for Code Representation

conference-paper Full Text OpenAlex 596 FWCI126.1448

Exploring the potential of using an AI language model for automated essay scoring

article Full Text OpenAlex 587 FWCI75.076

ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports

article Full Text OpenAlex 538 FWCI68.5532