LC-ICL improves few-shot NER and RE by using label-guided contrastive demonstrations that pair positive samples with error-annotated negative samples.
Retrieval-augmented code generation for universal information extraction
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
citing papers explorer
-
LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction
LC-ICL improves few-shot NER and RE by using label-guided contrastive demonstrations that pair positive samples with error-annotated negative samples.
-
Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text
Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
-
Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.