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Found 26 papers, 10 papers with code
Gradient-Free Adaptive Global Pruning for Pre-trained Language Models
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands.
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network.
ELAD: Explanation-Guided Large Language Models Active Distillation
The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences.
A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models
To address this, in this work, we introduce a Condensed Transition Graph Framework for Zero-Shot Link Prediction (CTLP), which encodes all the paths' information in linear time complexity to predict unseen relations between entities, attaining both efficiency and information preservation.
Uncertainty Quantification for In-Context Learning of Large Language Models
Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.
Gene-associated Disease Discovery Powered by Large Language Models
The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.
Open-ended Commonsense Reasoning with Unrestricted Answer Scope
In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision.
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples.
Ranked #6 on
Open Information Extraction
on OIE2016
Helper Recommendation with seniority control in Online Health Community
Online health communities (OHCs) are forums where patients with similar conditions communicate their experiences and provide moral support.
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This work aims to serve as a valuable resource for understanding the potential and opportunities of HKG in health research.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
Deep Graph Representation Learning and Optimization for Influence Maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users.
Knowledge-enhanced Neural Machine Reasoning: A Review
Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications.
Saliency-Augmented Memory Completion for Continual Learning
Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding.
DeepGAR: Deep Graph Learning for Analogical Reasoning
As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i. e., correspondence) in the target graph that is aligned with the base graph.
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems
Different from most traditional source localization methods, this paper focuses on a probabilistic manner to account for the uncertainty of different candidate sources.
Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks
Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change.
Slapping Cats, Bopping Heads, and Oreo Shakes: Understanding Indicators of Virality in TikTok Short Videos
We do so vis-\`a-vis three research hypotheses; namely, that: 1) the video content, 2) TikTok's recommendation algorithm, and 3) the popularity of the video creator contribute to virality.
"Sparse + Low-Rank'' Tensor Completion Approach for Recovering Images and Videos
By the multi-dimensional nature of color images and videos, in this paper, we propose a novel tensor completion approach, which is able to efficiently explore the sparsity of tensor data under the discrete cosine transform (DCT).
Low-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion
Our model possesses a sparse regularization term to promote a sparse core tensor of the Tucker decomposition, which is beneficial for tensor data compression.
"Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19
Finally, we find interesting differences in the context in which words related to Chinese people are used on the Web before and after the COVID-19 outbreak: on Twitter we observe a shift towards blaming China for the situation, while on /pol/ we find a shift towards using more (and new) Sinophobic slurs.
Word Embeddings
Social and Information Networks
Computers and Society
SocialGrid: A TCN-enhanced Method for Online Discussion Forecasting
Based on the nature of the grid, we leverage the Temporal Convolution Network to learn the dynamics at the grid level.
Social and Information Networks
NesTPP: Modeling Thread Dynamics in Online Discussion Forums
Online discussion forum creates an asynchronous conversation environment for online users to exchange ideas and share opinions through a unique thread-reply communication mode.
Social and Information Networks
Time-constrained Adaptive Influence Maximization
The well-known influence maximization problem aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process.
Social and Information Networks
Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation
Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items.
