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Found 14 papers, 3 papers with code
Demonstrating Mutual Reinforcement Effect through Information Flow
The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks.
Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening
Analysis of the decision-making efficacy of the LLM agents in the final offer stage further underscores the potential of LLM agents in transforming resume screening processes.
Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification
The proliferation of Large Language Models (LLMs) has spurred extensive research into LLM-related Prompt investigations, such as Instruction Learning (IL), In-context Learning (ICL), and Chain-of-Thought (CoT).
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect
In this vein, we introduce the General Information Extraction Large Language Model (GIELLM), which integrates text Classification, Sentiment Analysis, Named Entity Recognition, Relation Extraction, and Event Extraction using a uniform input-output schema.
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks
In this study, we propose an integrative analysis, converging sentence classification with Named Entity Recognition, with the objective to unveil and comprehend the mutual reinforcement effect within these two information extraction subtasks.
Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition
Information extraction(IE) is a crucial subfield within natural language processing.
Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks
In light of these findings, we conclude by proposing potential research trajectories to further enhance the development and performance of Large Language Models in their current stage.
A Few-shot Approach to Resume Information Extraction via Prompts
Also, we enhanced the verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt template design across NLP tasks.
Construction of English Resume Corpus and Test with Pre-trained Language Models
Moreover, one of the most critical application scenarios of information extraction is the information extraction of resumes.
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis
In this paper, we propose a new deep learning paradigm to assimilate the differences between languages for MSA.
