12 citations · 18 across the 6 of their papers we have counts for
6 papers
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…
AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK government transactions
Vincent J. Straub, Youmna Hashem, Jonathan Bright +5
There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex…
Generative AI is already widespread in the public sector
Jonathan Bright, Florence E. Enock, Saba Esnaashari +3
Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of…
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…
Approaches to the Algorithmic Allocation of Public Resources: A Cross-disciplinary Review
Saba Esnaashari, Jonathan Bright, John Francis +3
Allocation of scarce resources is a recurring challenge for the public sector: something that emerges in areas as diverse as healthcare, disaster recovery, and social welfare. The…