LC-ICL improves few-shot NER and RE by using label-guided contrastive demonstrations that pair positive samples with error-annotated negative samples.
InProceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 15372–15389, Singapore
6 Pith papers cite this work, alongside 17 external citations. Polarity classification is still indexing.
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representative citing papers
Many-shot ICL with LLMs matches or exceeds supervised BERT on NER and generates high-quality labels for low-resource settings, producing ~10% absolute F1 gains when used to fine-tune BERT.
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.
PhRAG applies NER and hybrid RAG to pool fragmented industrial spare parts data into a searchable virtual stock with natural language query support.
ProUIE uses macro-level complete modeling, meso-level streamlined alignment, and micro-level deep exploration with GRPO and stepwise rewards to improve LLM universal information extraction on 36 datasets without added external information.
citing papers explorer
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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.
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Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
Many-shot ICL with LLMs matches or exceeds supervised BERT on NER and generates high-quality labels for low-resource settings, producing ~10% absolute F1 gains when used to fine-tune BERT.
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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.
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Automating Information Extraction and Retrieval for Industrial Spare Parts Pooling
PhRAG applies NER and hybrid RAG to pool fragmented industrial spare parts data into a searchable virtual stock with natural language query support.
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ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction
ProUIE uses macro-level complete modeling, meso-level streamlined alignment, and micro-level deep exploration with GRPO and stepwise rewards to improve LLM universal information extraction on 36 datasets without added external information.
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