Large Language Models (LLMs) are increasingly capable of generating text that challenges human performance in domains requiring creativity, yet evaluating creativity in LLM-generated content remains a significant challenge. Here, we investigate whether current automatic evaluation methods can reliably capture human jud...
Alessandro Tutone, Giorgio Franceschelli, Mirco Musolesi· 1 citation
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 property. Real users, however, communicate political signals through their terminology, assumptions, and personal context. We investigate w...
Olivia Macmillan-Scott, Michael Jacobs, Nils W. Metternich et al.· 0 citations
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 values can depend on the context in which a decision is made. In this paper, we ask whether large language models (LLMs) appropriately adapt th...
Recent years have seen a growing discrepancy in the field of AI alignment: research and policy recommendations on AI ethics tend to assume a general set of ethical values, yet proliferating practice-specific uses of AI systems on the ground - in the legal, medical and translation domains, among others - have been effec...
Experimental results show that the number of neighbours per node and the average path length are the main factors affecting the emergence of cooperation in scenarios in which each agent learns to play the two-player Iterated Prisoner's Dilemma using deep reinforcement learning.
Seongho Son, Stephen Hailes, Mirco Musolesi· 0 citations
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