12 citations · 18 across the 5 of their papers we have counts for
3 papers · 1 filter
MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks
John Francis, Saba Esnaashari, Anton Poletaev +3
Large language models (LLMs) have demonstrated remarkable capabilities in text analysis tasks, yet their evaluation on complex, real-world applications remains challenging. We defi…
Evidence of a log scaling law for political persuasion with large language models
Kobi Hackenburg, Ben M. Tappin, Paul Röttger +3
Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with…
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
Leonardo Castro-Gonzalez, Yi-Ling Chung, Hannak Rose Kirk +4
The field of machine learning has recently made significant progress in reducing the requirements for labelled training data when building new models. These `cheaper' learning tech…