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Many-shot ICL matches BERT performance in NER tasks

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of many-shot in-context learning (ICL) for Named Entity Recognition (NER) using large language models (LLMs). The study found that by scaling ICL to hundreds of examples, LLMs can achieve performance comparable to or exceeding that of fine-tuned BERT models. Furthermore, the research demonstrates that many-shot ICL can be utilized as a data annotation framework, generating high-quality labeled data that leads to significant improvements in low-resource NER tasks. AI

IMPACT Demonstrates that scaling in-context learning can improve LLM performance on structured tasks like NER, potentially reducing the need for extensive fine-tuning.

RANK_REASON Research paper detailing a new methodology for LLM performance in NER. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Many-shot ICL matches BERT performance in NER tasks

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Research paper detailing a new methodology for LLM performance in NER. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Eduard Dragut ·

    Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition

    In-context learning (ICL) with large language models (LLMs) has emerged as a powerful alternative to fine-tuning for Named Entity Recognition (NER), achieving strong performance with minimal annotation and no additional training. However, prior work has shown that despite their a…

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