Search Results for author:
Found 5 papers, 3 papers with code
How Will I Argue? A Dataset for Evaluating Recommender Systems for Argumentations
Exchanging arguments is an important part in communication, but we are often flooded with lots of arguments for different positions or are captured in filter bubbles.
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
In this work, we present two different ways to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions: first, we show that augmenting a small fine-tuning dataset with synthetic data can improve the performance of the stance detection model.
AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs
In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post.
A Survey on Self-Supervised Representation Learning
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning.
