51 citations · 103 across the 10 of their papers we have counts for
10 papers
Inferring Private Personal Attributes of Virtual Reality Users from Head and Hand Motion Data
Vivek Nair, Christian Rack, Wenbo Guo +8
Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demon…
The False Promise of Imitating Proprietary LLMs
Arnav Gudibande, Eric Wallace, Charlie Snell +5
An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Inst…
ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management
Peng Gao, Xiaoyuan Liu, Edward Choi +3
Open-source cyber threat intelligence (OSCTI) has become essential for keeping up with the rapidly changing threat landscape. However, current OSCTI gathering and management soluti…
Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang +2
Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token sequence wi…
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models
Chenguang Wang, Xiao Liu, Dawn Song
We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, ma…
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Ye Yuan +5
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM…
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
Mantas Mazeika, Eric Tang, Andy Zou +6
In recent years, deep neural networks have demonstrated increasingly strong abilities to recognize objects and activities in videos. However, as video understanding becomes widely…
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Linyuan Gong +1
The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike…
Forecasting Future World Events with Neural Networks
Andy Zou, Tristan Xiao, Ryan Jia +7
Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision ma…
DeepStruct: Pretraining of Language Models for Structure Prediction
Chenguang Wang, Xiao Liu, Zui Chen +3
We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation,…