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Found 17 papers, 8 papers with code
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD.
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions.
Ranked #13 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs.
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning
In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks.
Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias
To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions.
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning
Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner.
Topology-Imbalance Learning for Semi-Supervised Node Classification
The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community.
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade
On the other hand, the exiting decisions made by internal classifiers are unreliable, leading to wrongly emitted early predictions.
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification
Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC).
Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction
Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction
In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement.
Recursive Graphical Neural Networks for Text Classification
Graph is a natural structure to describe the complicated relation between tokens.
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks.
Ranked #52 on
Node Classification
on Cora
Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model
In this work, we introduce a novel task: high-quality comment identification (HQCI), which aims to automatically assess the quality of online comments.
