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Found 5 papers, 2 papers with code
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.
Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
In this paper we adopt a representation-centric perspective on exploration in reinforcement learning, viewing exploration fundamentally as a density estimation problem.
Cyclophobic Reinforcement Learning
In environments with sparse rewards, finding a good inductive bias for exploration is crucial to the agent's success.
A Survey on Self-Supervised Representation Learning
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning.
