All articles tagged with #model accuracy
A report indicates that AI assistants misrepresent news content 45% of the time, mainly due to sourcing issues like incorrect citations and hallucinations, raising concerns about their reliability and the need for better source verification and critical evaluation skills.
The article discusses the nature of hallucinations in language models, emphasizing that not all outputs are hallucinations and that the term needs careful definition. It highlights the distinction between models predicting next tokens and generating false information, and debates whether all outputs can be considered hallucinations. The conversation also covers challenges in reducing hallucinations, the importance of proper evaluation, and philosophical questions about AI understanding and truth. Overall, it stresses that hallucinations are inherent to probabilistic models like LLMs, and efforts should focus on minimizing them rather than expecting complete elimination.
MIT researchers have developed a new privacy metric called Probably Approximately Correct (PAC) Privacy, which allows for the addition of minimal noise to machine-learning models while still protecting sensitive data. The researchers created an algorithm that automatically determines the optimal amount of noise to add, based on the uncertainty or entropy of the original data. This approach, unlike other privacy methods, does not require knowledge of the model's inner workings or training process. The PAC Privacy algorithm guarantees privacy even against adversaries with infinite computing power. While the technique does not indicate the accuracy loss caused by the added noise, it can be improved by creating more stable machine-learning models that produce consistent outputs with subsampled data.