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LLMs exhibit surprisingly human-like biases and overconfidence in math, revealed by a new dataset mapping their mathematical reasoning across diverse personas.
See how LLMs' stances on vaccines, disinformation, and gender equality shift when they "become" different people, thanks to a new dataset of 190,000 persona-driven debates.
TEA Nets reveal that LLMs express sadness with lower emotional intensity than humans in psychotherapy contexts, highlighting potential limitations in their ability to simulate genuine emotional responses.
Discover how GPT-4o, Claude, DeepSeek, and Mistral Large subtly shift human opinions on climate change, math anxiety, and health misinformation over weeks of conversation.
The findings reveal that the analysed LLMs adopt distinct communication styles and pronoun usage, even when prompted identically, highlighting the need for transparent, network-based methods to monitor and mitigate LLM biases as these models become central tools for retrieving information.
