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Found 10 papers, 8 papers with code
Qwen2.5 Technical Report
In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Ranked #7 on
on GPQA
HiLight: A Hierarchy-aware Light Global Model with Hierarchical Local ConTrastive Learning
However, the structure encoder has scale problem.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #3 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Semantic Human Mesh Reconstruction with Textures
SHERT applies semantic- and normal-based sampling between the detailed surface (e. g. mesh and SDF) and the corresponding SMPL-X model to obtain a partially sampled semantic mesh and then generates the complete semantic mesh by our specifically designed self-supervised completion and refinement networks.
LongQLoRA: Efficient and Effective Method to Extend Context Length of Large Language Models
We present LongQLoRA, an efficient and effective method to extend context length of large language models with less training resources.
FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training
We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics.
Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation
It can generalize well on the real-world data from all the other unseen views.
Similarity Based Auxiliary Classifier for Named Entity Recognition
The segmentation problem is one of the fundamental challenges associated with name entity recognition (NER) tasks that aim to reduce the boundary error when detecting a sequence of entity words.
Ranked #19 on
Named Entity Recognition (NER)
on WNUT 2017
Sequential Attention with Keyword Mask Model for Community-based Question Answering
So the QA pairs capture features and information from both question text and answer text, interacting and improving vector representations iteratively through hops.
