47 citations · 103 across the 27 of their papers we have counts for
29 papers
MoCo: A One-Stop Shop for Model Collaboration Research
Shangbin Feng, Yuyang Bai, Ziyuan Yang +17
Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multiple LMs collaborate, compose, an…
Instant Personalized Large Language Model Adaptation via Hypernetwork
Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen +8
Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) method…
Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment
Yizhuo Zhang, Heng Wang, Shangbin Feng +3
Previous research has sought to enhance the graph reasoning capabilities of LLMs by supervised fine-tuning on synthetic graph data. While these led to specialized LLMs better at so…
Can Large Language Models Understand Preferences in Personalized Recommendation?
Zhaoxuan Tan, Zinan Zeng, Qingkai Zeng +4
Large Language Models (LLMs) excel in various tasks, including personalized recommendations. Existing evaluation methods often focus on rating prediction, relying on regression err…
Machine Unlearning in Generative AI: A Survey
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan +2
Generative AI technologies have been deployed in many places, such as (multimodal) large language models and vision generative models. Their remarkable performance should be attrib…
Can LLM Graph Reasoning Generalize beyond Pattern Memorization?
Yizhuo Zhang, Heng Wang, Shangbin Feng +4
Large language models (LLMs) demonstrate great potential for problems with implicit graphical structures, while recent works seek to enhance the graph reasoning capabilities of LLM…
Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts
Zhaoxuan Tan, Zheyuan Liu, Meng Jiang
Personalized large language models (LLMs) aim to tailor interactions, content, and recommendations to individual user preferences. While parameter-efficient fine-tuning (PEFT) meth…
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
Herun Wan, Shangbin Feng, Zhaoxuan Tan +3
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 accu…
Towards Safer Large Language Models through Machine Unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan +2
The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptio…
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan +4
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making…
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
Zhaoxuan Tan, Qingkai Zeng, Yijun Tian +3
Personalization in large language models (LLMs) is increasingly important, aiming to align the LLMs' interactions, content, and recommendations with individual user preferences. Re…
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
Shangbin Feng, Herun Wan, Ningnan Wang +3
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we b…
User Modeling in the Era of Large Language Models: Current Research and Future Directions
Zhaoxuan Tan, Meng Jiang
User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The…
GADY: Unsupervised Anomaly Detection on Dynamic Graphs
Shiqi Lou, Qingyue Zhang, Shujie Yang +3
Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field…
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models
Yuyang Bai, Shangbin Feng, Vidhisha Balachandran +4
Large language models (LLMs) demonstrate remarkable performance on knowledge-intensive tasks, suggesting that real-world knowledge is encoded in their model parameters. However, be…
Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models
Wenxuan Ding, Shangbin Feng, Yuhan Liu +4
We propose Knowledge Crosswords, a geometric knowledge reasoning benchmark consisting of incomplete knowledge networks bounded by structured factual constraints, where LLMs are tas…
HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention
Sen Ye, Zhaoxuan Tan, Zhenyu Lei +4
Twitter bot detection has become an increasingly important and challenging task to combat online misinformation, facilitate social content moderation, and safeguard the integrity o…
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
Zijian Cai, Zhaoxuan Tan, Zhenyu Lei +4
As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial…
Can Language Models Solve Graph Problems in Natural Language?
Heng Wang, Shangbin Feng, Tianxing He +3
Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures, such as planning in robotics, multi-hop question answering or knowl…
BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Yuhan Liu, Zhaoxuan Tan, Heng Wang +3
Twitter bot detection has become a crucial task in efforts to combat online misinformation, mitigate election interference, and curb malicious propaganda. However, advanced Twitter…
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
Heng Wang, Wenqian Zhang, Yuyang Bai +4
Online movie review platforms are providing crowdsourced feedback for the film industry and the general public, while spoiler reviews greatly compromise user experience. Although p…
BotPercent: Estimating Bot Populations in Twitter Communities
Zhaoxuan Tan, Shangbin Feng, Melanie Sclar +4
Twitter bot detection is vital in combating misinformation and safeguarding the integrity of social media discourse. While malicious bots are becoming more and more sophisticated a…
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Shangbin Feng, Zhaoxuan Tan, Zilong Chen +5
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches ar…
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding
Shangbin Feng, Zhaoxuan Tan, Wenqian Zhang +2
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 downstr…