Local LLM Ensembles for Zero-shot Portuguese Named Entity Recognition

• URL: http://arxiv.org/abs/2512.10043v1
• Date: Wed, 10 Dec 2025 19:55:06 GMT
• Title: Local LLM Ensembles for Zero-shot Portuguese Named Entity Recognition
• Authors: João Lucas Luz Lima Sarcinelli, Diego Furtado Silva,
• Abstract summary: Large Language Models (LLMs) excel in many Natural Language Processing (NLP) tasks through in-context learning.<n>However, no single model dominates all tasks, motivating ensemble approaches.<n>This work proposes a novel three-step ensemble pipeline for zero-shot NER using similarly capable, locally run LLMs.
• Score: 0.7734726150561086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) excel in many Natural Language Processing (NLP) tasks through in-context learning but often under-perform in Named Entity Recognition (NER), especially for lower-resource languages like Portuguese. While open-weight LLMs enable local deployment, no single model dominates all tasks, motivating ensemble approaches. However, existing LLM ensembles focus on text generation or classification, leaving NER under-explored. In this context, this work proposes a novel three-step ensemble pipeline for zero-shot NER using similarly capable, locally run LLMs. Our method outperforms individual LLMs in four out of five Portuguese NER datasets by leveraging a heuristic to select optimal model combinations with minimal annotated data. Moreover, we show that ensembles obtained on different source datasets generally outperform individual LLMs in cross-dataset configurations, potentially eliminating the need for annotated data for the current task. Our work advances scalable, low-resource, and zero-shot NER by effectively combining multiple small LLMs without fine-tuning. Code is available at https://github.com/Joao-Luz/local-llm-ner-ensemble.

Related papers

• Assessment of Generative Named Entity Recognition in the Era of Large Language Models [11.887255148221008]
  Named entity recognition (NER) is evolving from a sequence labeling task into a generative paradigm with the rise of large language models (LLMs)<n>We conduct a systematic evaluation of open-source LLMs on both flat and nested NER tasks.<n>These findings demonstrate that generative NER with LLMs is a promising, user-friendly alternative to traditional methods.
  arXiv  Detail & Related papers  (2026-01-25T16:20:40Z)
• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language.<n>Currently, instruction-tuned large language models (LLMs) excel at various English tasks.<n>Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition [53.019885776033824]
  We propose DynamicNER, the first NER dataset designed for Large Language Models (LLMs)-based methods with dynamic categorization.<n>The dataset is also multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains.<n>Experiments show that DynamicNER serves as a robust and effective benchmark for LLM-based NER methods.
  arXiv  Detail & Related papers  (2024-09-17T09:32:12Z)
• Leveraging Open-Source Large Language Models for Native Language Identification [1.6267479602370543]
  Native Language Identification (NLI) has applications in forensics, marketing, and second language acquisition.<n>This study explores the potential of using open-source generative large language models (LLMs) for NLI.
  arXiv  Detail & Related papers  (2024-09-15T08:14:18Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding [78.36702055076456]
  This paper introduces Multi-scale Positional. (Ms-PoE) which is a simple yet effective plug-and-play approach to enhance the capacity of. LLMs to handle relevant information located in the middle of the context.
  arXiv  Detail & Related papers  (2024-03-05T04:58:37Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs. We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
  arXiv  Detail & Related papers  (2024-01-14T16:17:07Z)
• Generative Multimodal Entity Linking [24.322540112710918]
  Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to referent entities from a knowledge base. Existing MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. We propose GEMEL, a Generative Multimodal Entity Linking framework based on Large Language Models (LLMs) Our framework is compatible with any off-the-shelf language model, paving the way towards an efficient and general solution.
  arXiv  Detail & Related papers  (2023-06-22T07:57:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.