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Found 16 papers, 7 papers with code
AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
Offline batch inference is a common task in the industry for deep learning applications, but it can be challenging to ensure stability and performance when dealing with large amounts of data and complicated inference pipelines.
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Previous works only address part of the stragglers and could not adaptively solve various stragglers in practice.
Prompt-fused framework for Inductive Logical Query Answering
Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning.
InBox: Recommendation with Knowledge Graph using Interest Box Embedding
This homogenization limits the precise exploitation of knowledge graph data and interest connectivity.
Editing Conceptual Knowledge for Large Language Models
Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs).
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions.
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE).
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
To address the mentioned problems, we propose Adaptive Multi-modal Fusion and Modality Adversarial Training (AdaMF-MAT) to unleash the power of imbalanced modality information for MMKGC.
Unified Hallucination Detection for Multimodal Large Language Models
Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination.
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry.
A Comprehensive Study of Knowledge Editing for Large Language Models
In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches.
Ranked #1 on
knowledge editing
on zsRE
(using extra training data)
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
Furthermore, our framework provides a simple user interface and allows for the agile allocation of models across devices in a fine-grained manner for various training scenarios, involving models of varying sizes and devices of different scales.
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a memory-communication balanced strategy set Partial Redundancy Optimizer (PaRO).
InstructIE: A Bilingual Instruction-based Information Extraction Dataset
Large language models can perform well on general natural language tasks, but their effectiveness is still not optimal for information extraction.
An Integration and Operation Framework of Geothermal Heat Pumps in Distribution Networks
Besides, in the Virtual Power Plant (VPP) system, the demand response of clustered GHP systems can improve the operating flexibility of the power grid.
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification
We propose Crowd-based neural networks for Text Sentiment Classification (CrowdTSC for short).
