A retrieval-based in-context learning pipeline built on synthetic demonstrations achieves only modest entity and relation extraction scores in zero-shot document-level information extraction.
A survey on cutting-edge relation extraction techniques based on language models
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abstract
This comprehensive survey delves into the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences over the past four years, focusing on models that leverage language models. Our findings underscore the dominance of BERT-based methods in achieving state-of-the-art results for RE while also noting the promising capabilities of emerging large language models (LLMs) like T5, especially in few-shot relation extraction scenarios where they excel in identifying previously unseen relations.
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cs.CL 1years
2025 1verdicts
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DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
A retrieval-based in-context learning pipeline built on synthetic demonstrations achieves only modest entity and relation extraction scores in zero-shot document-level information extraction.