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Found 10 papers, 5 papers with code
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
Most previous approaches rely on the sentence-level retrieval and combination paradigm (retrieval of similar code snippets and use of the corresponding code and summary pairs) on the encoder side.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture
Based on such a decomposition, our architecture shares the central tensor across all layers for reducing the model size and meanwhile keeps layer-specific auxiliary tensors (also using adapters) for enhancing the adaptation flexibility.
TikTalk: A Multi-Modal Dialogue Dataset for Real-World Chitchat
Compared with previous image-based dialogue datasets, the richer sources of context in TikTalk lead to a greater diversity of conversations.
Bi-convolution matrix factorization algorithm based on improved ConvMF
With the rapid development of information technology, "information overload" has become the main theme that plagues people's online life.
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models
The results show that WuDaoMM can be applied as an efficient dataset for VLPMs, especially for the model in text-to-image generation task.
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models.
Image Dataset Compression Based on Matrix Product States
Based on the MPS structure, we propose a new dataset compression method that compresses datasets by filtering long-range correlation information in task-agnostic scenarios and uses dataset distillation to supplement the information in task-specific scenarios.
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
