The inferred user preference descriptions are used to define prompts for generating responses in the future.

This renders them impractical to use in a wide range of use-cases, limiting who can benefit from them to a handful of big corporations. As an attempt to mitigate this issue, Touvron et al.

The current paradigm of evaluating Large Language Models (LLMs) through static benchmarks comes with significant limitations, such as vulnerability to data contamination and a lack of adaptability to the evolving capabilities of LLMs.

PCWorld reports that OpenAI is launching new security features for ChatGPT, including Lockdown Mode and Elevated Risk labels to combat growing threats. Lockdown Mode restricts external interactions and disables web browsing for high-privacy users, while risk labels clearly mark potentially dangerous features. These updates specifically address prompt injection attacks where malicious prompts attempt to trick the AI into performing harmful actions. OpenAI is launching two new security features in ChatGPT to address growing threats to its AI systems, according to a recent blog post . As AI services increasingly connect to wider parts of the web and more external apps, the risk of so-called "prompt injection attacks" also increases.

To facilitate these annotations, we used both our user application and Label Studio.

While efforts have been made to explain computer vision models, these approaches often fall short of providing direct and human-understandable explanations.