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Sub-billion parameter SLMs outperform zero-shot GPT-5.4 and Claude Sonnet 4.6 on relation extraction benchmarks

A new arXiv paper demonstrates that small language models (360M–3B parameters) fine-tuned on task-specific data can substantially outperform zero-shot frontier LLMs on relation extraction tasks. The best sub-billion model, Qwen2.5-0.5B fine-tuned on pooled general-domain data, achieves micro-F1 of 0.83 versus 0.69 for GPT-5.4 and 0.66 for Claude Sonnet 4.6 in zero-shot settings. The authors attribute the gains to task adaptation rather than model architecture, with a discriminative RoBERTa baseline also exceeding frontier models, and show that 4-bit quantized models deployable on consumer GPUs can match or beat proprietary API-based systems for this narrow task. The work provides evidence that for well-defined NLP tasks with available training data, compact adapted models offer a practical, private, and hardware-efficient alternative to frontier APIs.