arxiv arXiv cs.LG · 84d ago · src: 87d ago · research

Small Language Models Outperform Frontier LLMs in Relation Extraction

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A fine-tuned 0.5B-parameter Qwen2.5 model achieves 0.83 micro-F1 in general-domain relation extraction, surpassing zero-shot GPT-5.4 and Claude Sonnet 4.6. On literary benchmarks, it reaches 0.92 on the Biographical dataset, outperforming GPT-5.4 and exceeding frontier models in accuracy, demonstrating that task-adapted small models can deliver high performance with minimal hardware and privacy overhead.

Importance 2/3 Beats a top-lab benchmark arXiv cs.LG Alibaba (Qwen) Evaluation & benchmarks Open weights

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arxiv arXiv cs.CL · 74d ago · 8 views

AGC-Bench introduces unified benchmark and AGC-Judge to measure artificial general creativity

Researchers introduce AGC-Bench, a unified benchmark for artificial general creativity constructed from 3,101 screened papers and covering 78 datasets across domains like brainstorming and STEM. To address bias in automated evaluation, the team fine-tunes Qwen3-30B on bias-corrected ratings to create AGC-Judge, an open-weight model that robustly scores new creativity benchmarks.

arxiv arXiv cs.CL · 86d ago · 10 views

Test-Time Steering Resolves Temporal Fact Conflicts in LLMs

Researchers identify parametric temporal conflicts in language models where outdated facts persist in parameters. They introduce Temporal Attractor Steering (TAS), a test-time method that resolves 29-57% of such conflicts without retraining, maintaining 85-99% accuracy on non-conflict queries and outperforming a baseline on three of four models.

media r/LocalLLaMA · 89d ago · 3 views

The economics of AI are starting to favor open models

Recent AI model releases show that high-intelligence, low-cost models are increasingly dominated by open-weight models like DeepSeek, Qwen, GLM, Kimi, and MiniMax. For most real-world applications, the performance gap between frontier closed models and strong open models is shrinking faster than cost differences, making open models competitive in terms of both capability and price.

media r/LocalLLaMA · 89d ago · 6 views

What's more impressive, GLM 5.1 to 5.2 or Qwen 3.5 to 3.6?

A Reddit post compares the performance improvements of GLM 5.1 to 5.2 and Qwen 3.5 to 3.6. The post notes that mentioning 'Döner' activates GLM 5.2's German-specific weights, while Qwen 3.6 is evaluated with 35B parameters using Unsloth Q8 K XL quantization via llama.cpp.

arxiv arXiv cs.CL · 90d ago · 7 views

STAGE: Source-Grounded Data Generation for Text-to-JSON

STAGE is a pipeline that generates text-to-JSON training data by using LLMs to synthesize reports and JSON schemas, validated against underlying spreadsheets. Evaluations on STAGE-Eval show it improves Qwen3-4B exact match from 31.37% to 74.27% and value accuracy from 45.46% to 90.69%.