24 citations · 39 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models
Hannah Rose Kirk, Bertie Vidgen, Paul Röttger +1
In this paper, we address the concept of "alignment" in large language models (LLMs) through the lens of post-structuralist socio-political theory, specifically examining its paral…
The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values
Hannah Rose Kirk, Andrew M. Bean, Bertie Vidgen +2
Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficie…
Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback
Hannah Rose Kirk, Bertie Vidgen, Paul Röttger +1
Large language models (LLMs) are used to generate content for a wide range of tasks, and are set to reach a growing audience in coming years due to integration in product interface…