AI chatbots trained to be warm and friendly when interacting with users may also be more prone to inaccuracies, new research suggests. Oxford Internet Institute (OII) researchers analysed more than 400,000 responses from five AI systems which had been tweaked to communicate in a more empathetic way. Friendlier answers contained more mistakes - from giving inaccurate medical advice to reaffirming user's false beliefs, the study found. The findings raise further questions over the trustworthiness of AI models, which are often deliberately designed to be warm and human-like in order to increase engagement. Such concerns are accentuated by AI chatbots being used for support and even intimacy, as developers seek to broaden their appeal.

Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented.

Our code is available at GitHub.

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set.

CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in text-guided attention. Building upon this observation, we propose a simple yet effective strategy: Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR). This framework incorporates two components: the Attention Refinement module and the Attention-based Model Constraint module.