The development of artificial intelligence, particularly large language models (LLMs), has raised concerns about cybersecurity laws. Scholars at Harvard highlighted at the Black Hat conference that the U.S. Computer Fraud and Abuse Act (CFAA) fails to effectively protect AI security researchers, exposing them to legal risks. They noted that existing laws are vague regarding actions like 'prompt injection attacks,' making it difficult for research....

In the field of natural language processing, large language models perform exceptionally well on complex tasks, but model evaluation heavily relies on expensive and time-consuming human-annotated data. As models advance, the utility of existing data declines, necessitating the continuous collection of new data for scalable and sustainable evaluation. The Meta FAIR research team has proposed the 'Self-Taught Evaluator' to address this issue. This innovative approach trains on synthetic data, eliminating the need for human annotation by generating contrasting synthetic preferences.