47 citations · 60 across the 34 of their papers we have counts for
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ContextAdapt: Evaluating Contextual Adaptation and Value Alignment in LLMs
Olivia Macmillan-Scott, Mirco Musolesi
Values such as honesty, autonomy, and confidentiality are often regarded as general principles underpinning AI alignment. However, what it means to act in accordance with these val…
Framing the Narrative: Ideological Mimicry in Large Language Models
Olivia Macmillan-Scott, Michael Jacobs, Nils Metternich +1
Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's stance as a relatively stable…
The Limits of Automatic Evaluation of Creativity in Large Language Models
Alessandro Tutone, Giorgio Franceschelli, Mirco Musolesi
Large Language Models (LLMs) are increasingly capable of generating text that challenges human performance in domains requiring creativity, yet evaluating creativity in LLM-generat…
DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation
Giorgio Franceschelli, Mirco Musolesi
Despite their growing capabilities, language models still frequently reproduce content from their training data, generate repetitive text, and favor common grammatical patterns and…
Thinking Outside the (Gray) Box: A Context-Based Score for Assessing Value and Originality in Neural Text Generation
Giorgio Franceschelli, Mirco Musolesi
Despite the increasing use of large language models for creative tasks, their outputs often lack diversity. Common solutions, such as sampling at higher temperatures, can compromis…
(Ir)rationality and Cognitive Biases in Large Language Models
Olivia Macmillan-Scott, Mirco Musolesi
Do large language models (LLMs) display rational reasoning? LLMs have been shown to contain human biases due to the data they have been trained on; whether this is reflected in rat…