Search Results for author:
Found 12 papers, 6 papers with code
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
In day-to-day communication, people often approximate the truth - for example, rounding the time or omitting details - in order to be maximally helpful to the listener.
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics.
Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed.
Improving Interpersonal Communication by Simulating Audiences with Language Models
How do we communicate with others to achieve our goals?
API-Assisted Code Generation for Question Answering on Varied Table Structures
A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms.
LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
We reflect on human and LLMs' different sensitivities to instructions, stress the importance of enabling human-facing safeguards for LLMs, and discuss the potential of training humans and LLMs with complementary skill sets.
ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing
We find that across 119 {checklist question, paper} pairs, the LLM had an 86. 6% accuracy.
Hierarchical Graph Neural Networks for Particle Track Reconstruction
We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN).
Proposing a System Level Machine Learning Hybrid Architecture and Approach for a Comprehensive Autism Spectrum Disorder Diagnosis
Finally, we implemented our hybrid model by incorporating different features of the Support Vector Machine and the DenseNet into one model.
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design
Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted.
Developing a New Autism Diagnosis Process Based on a Hybrid Deep Learning Architecture Through Analyzing Home Videos
Our novel architecture is able to effectively automate ASD pre-screening with a maximum weighted accuracy of 84%.
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case.
