Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Paper • 2506.03295 • Published Jun 3 • 17
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines Paper • 2502.14739 • Published Feb 20 • 106
ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations Paper • 2504.00824 • Published Apr 1 • 44
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series Paper • 2405.19327 • Published May 29, 2024 • 49
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents Paper • 2406.13923 • Published Jun 20, 2024 • 24
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark Paper • 2409.02813 • Published Sep 4, 2024 • 32
MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks Paper • 2410.10563 • Published Oct 14, 2024 • 39
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale Paper • 2412.05237 • Published Dec 6, 2024 • 48
Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate Paper • 2501.17703 • Published Jan 29 • 59
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark Paper • 2406.01574 • Published Jun 3, 2024 • 51
Augmenting Black-box LLMs with Medical Textbooks for Clinical Question Answering Paper • 2309.02233 • Published Sep 5, 2023 • 1