Fake News
In this project, we aim to improve the automatic detection of false information. We rely on trustworthy sources and evaluate content against them.
Tal Schuster
PhD Student
Publications
Consistent Accelerated Inference via Confident Adaptive Transformers
We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now …
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as …
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction …
Distilling the Evidence to Augment Fact Verification Models
The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the …
The Limitations of Stylometry for Detecting Machine-Generated Fake News
Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading …
Automatic Fact-guided Sentence Modification
Online encyclopediae like Wikipedia contain large amounts of text that need frequent corrections and updates. The new information may …
Towards Debiasing Fact Verification Models
Fact verification requires validating a claim in the context of evidence. We show, however, that in the popular FEVER dataset this …
