Trending Research
MarkLLM: An Open-Source Toolkit for LLM Watermarking
thu-bpm/markllm
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However, the abundance of LLM watermarking algorithms, their intricate mechanisms, and the complex evaluation procedures and perspectives pose challenges for researchers and the community to easily experiment with, understand, and assess the latest advancements.
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
hitsz-tmg/umoe-scaling-unified-multimodal-llms
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Although the Mixture of Experts (MoE) architecture has been employed to efficiently scale large language and image-text models, these efforts typically involve fewer experts and limited modalities.
KAN: Kolmogorov-Arnold Networks
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs).
Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head
om-ai-lab/OmDet
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End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set and open-vocabulary object detection (OVD) tasks through the integration of language modalities.
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
However, unlike pretraining or fine-tuning a single model, scaling reinforcement learning from human feedback (RLHF) for training large language models poses coordination challenges across four models.
MambaOut: Do We Really Need Mamba for Vision?
For vision tasks, as image classification does not align with either characteristic, we hypothesize that Mamba is not necessary for this task; Detection and segmentation tasks are also not autoregressive, yet they adhere to the long-sequence characteristic, so we believe it is still worthwhile to explore Mamba's potential for these tasks.
How Far Are We From AGI
The evolution of artificial intelligence (AI) has profoundly impacted human society, driving significant advancements in multiple sectors.
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
mlc-ai/web-llm
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Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives.
Efficient Multimodal Large Language Models: A Survey
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.
Your Transformer is Secretly Linear
AIRI-Institute/LLM-Microscope
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This regularization improves performance metrics on benchmarks like Tiny Stories and SuperGLUE and as well successfully decreases the linearity of the models.
