Search Results for author:
Found 18 papers, 13 papers with code
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accuracy is paramount.
Towards Safer Large Language Models through Machine Unlearning
To address this gap, we introduce Selective Knowledge negation Unlearning (SKU), a novel unlearning framework for LLMs, designed to eliminate harmful knowledge while preserving utility on normal prompts.
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering.
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
OPPU integrates parametric user knowledge in the personal PEFT parameters with the non-parametric knowledge acquired through retrieval and profile.
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection.
User Modeling in the Era of Large Language Models: Current Research and Future Directions
Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions.
GADY: Unsupervised Anomaly Detection on Dynamic Graphs
Supplementary experiments further validate the effectiveness of our model design and the necessity of each module.
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models
To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains.
Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models
We additionally propose two new approaches, Staged Prompting and Verify-All, to augment LLMs' ability to backtrack and verify structured constraints.
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
For datasets without graph structure, we simply replace the GNN with an MLP, which has also shown strong performance.
Can Language Models Solve Graph Problems in Natural Language?
We then propose Build-a-Graph Prompting and Algorithmic Prompting, two instruction-based approaches to enhance LLMs in solving natural language graph problems.
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms.
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction.
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding
With the advent of pretrained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks.
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach
In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework.
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
Legislator Representation Learning with Social Context and Expert Knowledge
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks.
