8 papers · 1 filter
Tailored untruths: How personalisation challenges LLM safeguards
João A. Leite, Arnav Arora, Silvia Gargova +5
Large Language Models (LLMs) can generate highly persuasive disinformation, yet little is known about how effectively they personalise it across languages and demographic groups. W…
Revealing Fine-Grained Values and Opinions in Large Language Models
Dustin Wright, Arnav Arora, Nadav Borenstein +3
Uncovering latent values and opinions embedded in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by prompting…
Probing Pre-Trained Language Models for Cross-Cultural Differences in Values
Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein
Language embeds information about social, cultural, and political values people hold. Prior work has explored social and potentially harmful biases encoded in Pre-Trained Language…
Investigating Human Values in Online Communities
Nadav Borenstein, Arnav Arora, Lucie-Aimée Kaffee +1
Studying human values is instrumental for cross-cultural research, enabling a better understanding of preferences and behaviour of society at large and communities therein. To stud…
Multi-Modal Framing Analysis of News
Arnav Arora, Srishti Yadav, Maria Antoniak +2
Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its recep…
A Reality Check on Context Utilisation for Retrieval-Augmented Generation
Lovisa Hagström, Sara Vera MarjanoviÄ, Haeun Yu +5
Retrieval-augmented generation (RAG) helps address the limitations of parametric knowledge embedded within a language model (LM). In real world settings, retrieved information can…