S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models Paper • 2310.15147 • Published Oct 23, 2023 • 2
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models Paper • 2402.12851 • Published Feb 20, 2024 • 2
Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent Paper • 2402.13717 • Published Feb 21, 2024 • 2
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments Paper • 2404.07972 • Published Apr 11, 2024 • 48
Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? Paper • 2407.10956 • Published Jul 15, 2024 • 7
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows Paper • 2411.07763 • Published Nov 12, 2024
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models Paper • 2410.07331 • Published Oct 9, 2024 • 5
Unlocking Continual Learning Abilities in Language Models Paper • 2406.17245 • Published Jun 25, 2024 • 30
Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training Paper • 2405.15319 • Published May 24, 2024 • 28
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models Paper • 2402.12851 • Published Feb 20, 2024 • 2
Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent Paper • 2402.13717 • Published Feb 21, 2024 • 2
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark Paper • 2401.11944 • Published Jan 22, 2024 • 27