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Small LLMs rival frontier models in relation extraction tasks

By PulseAugur Editorial · [3 sources] ·

A new research paper explores the effectiveness of large language models (LLMs) for cross-lingual relation extraction, specifically focusing on Romanian. The study found that while LLMs like Gemma 4 31B show a performance drop compared to English in zero-shot and few-shot settings, fine-tuning with QLoRA significantly improves results and reduces the cross-lingual gap. The research also highlights that smaller, task-adapted models, such as Qwen2.5-0.5B, can rival or even surpass the performance of larger, general-purpose frontier LLMs like GPT-5.4 and Claude Sonnet 4.6 on specific relation extraction tasks, especially when computational resources are a concern. AI

IMPACT Task-adapted smaller models can outperform larger frontier models on specific tasks, enabling efficient and private deployment.

RANK_REASON The cluster contains two arXiv papers detailing research on relation extraction using LLMs and smaller models.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Small LLMs rival frontier models in relation extraction tasks

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Dragos-Mitrut Vasile, Elena-Simona Apostol, Stefan-Adrian Toma, Adrian Paschke, Ciprian-Octavian Truica ·

    Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

    arXiv:2606.31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross-lingual RE for Romanian by combining automatic dataset translation with large l…

  2. arXiv cs.AI TIER_1 English(EN) · Ciprian-Octavian Truica ·

    Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

    Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross-lingual RE for Romanian by combining automatic dataset translation with large language model (LLM) inference. We translate the Se…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction

    Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deployment in resource-constrained or privacy-sensitive settings. We investigate how far small language models (SLMs) can close this gap ac…

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