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In-Context Learning

Papers

Showing 2650 of 2297 papers

TitleStatusHype
SegGPT: Segmenting Everything In ContextCode4
AudioLDM 2: Learning Holistic Audio Generation with Self-supervised PretrainingCode4
Otter: A Multi-Modal Model with In-Context Instruction TuningCode4
Eureka: Human-Level Reward Design via Coding Large Language ModelsCode4
MIMIC-IT: Multi-Modal In-Context Instruction TuningCode4
InternLM-Math: Open Math Large Language Models Toward Verifiable ReasoningCode4
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQLCode4
Images Speak in Images: A Generalist Painter for In-Context Visual LearningCode4
VILA: On Pre-training for Visual Language ModelsCode4
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningCode4
WavCraft: Audio Editing and Generation with Large Language ModelsCode4
Zero-shot forecasting of chaotic systemsCode4
Generative Multimodal Models are In-Context LearnersCode3
Scaling Diffusion Language Models via Adaptation from Autoregressive ModelsCode3
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language ModelsCode3
Fine-Tuning Language Models with Just Forward PassesCode3
AutoTimes: Autoregressive Time Series Forecasters via Large Language ModelsCode3
Foundation Models for Music: A SurveyCode3
The Surprising Effectiveness of Test-Time Training for Few-Shot LearningCode3
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with TextCode3
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human SupervisionCode3
Embodied CoT Distillation From LLM To Off-the-shelf AgentsCode3
PromptDresser: Improving the Quality and Controllability of Virtual Try-On via Generative Textual Prompt and Prompt-aware MaskCode3
QuRating: Selecting High-Quality Data for Training Language ModelsCode3
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different ScenesCode3
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