Neural Information Processing Systems http://nips.cc/

Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs).

In addition, users may become overly dependent on the model's outputs For the feedback, we ask the person "Please consider the quality of the Given a score (1-5). 1 means its quality is bad, and 5 means its quality is very good". The interface of the user study is shown in Fig. A1. We report the average scores in Tab. We have a total of 1.1M training data in FIRE. In Fig. A2, we present the curves of A T, A TR, A TR, and RR using different Results show that more data leads to better performance.

Data analysis is a crucial analytical process essential for deriving insights from real-world databases.

INDICT: a new framework that empowers LLMs with Internal Dialogues of Critiques for both safety and helpfulness guidance.

To mitigate this gap, we first refine and extend the definition of honesty inLLMs based onthedefinition proposed byAskelletal.[14],astheability

The Only Thing Standing Between Humanity and AI Apocalypse Is Claude? As AI systems grow more powerful, Anthropic's resident philosopher says the startup is betting Claude itself can learn the wisdom needed to avoid disaster. Anthropic is locked in a paradox: Among the top AI companies, it's the most obsessed with safety and leads the pack in researching how models can go wrong. But even though the safety issues it has identified are far from resolved, Anthropic is pushing just as aggressively as its rivals toward the next, potentially more dangerous, level of artificial intelligence. Its core mission is figuring out how to resolve that contradiction. Last month, Anthropic released two documents that both acknowledged the risks associated with the path it's on and hinted at a route it could take to escape the paradox.