Preface to the Special Issue of the TAL Journal on Scholarly Document Processing
원문 초록
The rapid growth of scholarly literature makes it increasingly difficult for researchers to keep up with new knowledge. Automated tools are now more essential than ever to help navigate and interpret this vast body of information. Scientific papers pose unique difficulties, with their complex language, specialized terminology, and diverse formats, requiring advanced methods to extract reliable and actionable insights. Large language models (LLMs) offer new opportunities, enabling tasks such as literature reviews, writing assistance, and interactive exploration of research. This special issue of the TAL journal highlights research addressing these challenges and, more broadly, research on natural language processing and information retrieval for scholarly and scientific documents.
함께 살펴볼 논문
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ScholarSearch: Benchmarking Scholar Searching Ability of LLMs
JointRank: Rank Large Set with Single Pass
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
토론
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