Auto-Constriction Turning for Multilingual Neural Machine Translation: A Novel Approach
Auto-Constriction Turning (ACT-MNMT) improves multilingual machine translation by addressing off-target issues and enhancing model understanding.
Effektive Personalisierung von LoRA für textzentriertes Textverständnis
Effektive und effiziente Anpassung von vortrainierten Sprachmodellen für textzentriertes Verständnis.
Kette des Denkens mit expliziter Beweisführung für Few-shot-Beziehungsextraktion
CoT-ER ist eine effektive Methode für Few-shot-Beziehungsextraktion, die auf in-context Learning basiert und übertrifft andere Baseline-Methoden.
Few-shot Relation Extraction with CoT-ER Approach
Large language models can effectively perform few-shot relation extraction tasks with the CoT-ER approach, outperforming fully-supervised methods.
ROUGE-K: Keyword-Oriented Summarization Evaluation Metric
시스템 생성 요약에 키워드가 포함되어 있는지를 평가하는 ROUGE-K 키워드 지향 평가 메트릭이 중요하다.
RST Discourse Parsing with Large Language Models: Impact and Results
Large Language Models (LLMs) significantly impact RST discourse parsing, achieving state-of-the-art results.
SemEval 2024 Task 3: Multimodal Emotion Cause Analysis Approach
텍스트, 오디오, 비디오를 통합한 감정 원인 분석의 중요성
Unsupervised Pretraining for Fact Verification by Language Model Distillation at ICLR 2024
Unsupervised pretraining framework SFAVEL achieves state-of-the-art results in fact verification by distilling language model features.
Neuer Ansatz für die Bewertung von Natural Language Preprocessing-Systemen
Ein neuer Ansatz für die Bewertung von Natural Language Preprocessing-Systemen wird vorgestellt, um die Leistung fair und zuverlässig zu bewerten.
GoLLIE: Annotation Guidelines for Zero-Shot Information Extraction
Large Language Models can improve zero-shot information extraction by following annotation guidelines.
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