8 papers
More Computational Resources Do Not Ensure Higher Scholarly Impact: Evidence from Leading NLP Conference Papers
Shuai Chen, Tong Bao, Jitong Peng +1
Computational resources are increasingly central to NLP research, but how closely reported GPU capability aligns with scholarly impact remains unclear. We analyze 13,921 ACL, EMNLP…
Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach
Tong Bao, Yi Zhao, Heng Zhang +1
Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, large language models (LLMs) ha…
Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI
Wenqing Wu, Chengzhi Zhang, Yi Zhao +1
With the rapid advancement of Large Language Models (LLMs), the academic community has faced unprecedented disruptions, particularly in the realm of academic communication. The pri…
Beyond Single-Dimension Novelty: How Combinations of Theory, Method, and Results-based Novelty Shape Scientific Impact
Yi Zhao, Yang Chenggang, Yuzhuo Wang +3
Scientific novelty drives advances at the research frontier, yet it is also associated with heightened uncertainty and potential resistance from incumbent paradigms, leading to com…
SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models
Tong Bao, Mir Tafseer Nayeem, Davood Rafiei +1
Automatic survey generation has emerged as a key task in scientific document processing. While large language models (LLMs) have shown promise in generating survey texts, the lack…
SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers
Wenqing Wu, Chengzhi Zhang, Tong Bao +1
Novelty is a core component of academic papers, and there are multiple perspectives on the assessment of novelty. Existing methods often focus on word or entity combinations, which…