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Found 8 papers, 4 papers with code
MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts
Large language models like ChatGPT have shown substantial progress in natural language understanding and generation, proving valuable across various disciplines, including the medical field.
Fraud Detection with Binding Global and Local Relational Interaction
Apart from the Transformer-based network, we further introduce a Relation-Aware GNN module to learn global embeddings, which is later merged into the local embeddings by an attention fusion module and a skip connection.
ARKS: Active Retrieval in Knowledge Soup for Code Generation
Recently the retrieval-augmented generation (RAG) paradigm has raised much attention for its potential in incorporating external knowledge into large language models (LLMs) without further training.
Linear Attention via Orthogonal Memory
Given that orthogonal memory compresses global information, we further dissect the context to amplify fine-grained local information.
Attentive Multi-Layer Perceptron for Non-autoregressive Generation
Furthermore, we marry AMLP with popular NAR models, deriving a highly efficient NAR-AMLP architecture with linear time and space complexity.
SelfEvolve: A Code Evolution Framework via Large Language Models
However, while various methods have been proposed to augment LLMs with retrieved knowledge and enhance the quality of code generation, the performance of these retrieval-based methods is limited by the strength of the retrievers used.
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
In this paper, we propose Comprehensive Attention Benchmark (CAB) under a fine-grained attention taxonomy with four distinguishable attention patterns, namely, noncausal self, causal self, noncausal cross, and causal cross attentions.
Efficient 3D Deep LiDAR Odometry
An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper.
