We conduct a preliminary study on finding a proper prompt construction strategy. Further research can utilize our analysis to construct more powerful code prompts. Table 1: Code prompt study results of CBS. N" means there are one human-relevant function Table 2: Automatic and human evaluation results of social biases in the generated code on GPT -4. We also conduct experiments on GPT -4.

Deceptive agents are a challenge for the safety, trustworthiness, and cooperation of AI systems.

Furthermore, we demonstrate that CQ can preservemodel quality reasonably with KV cache quantizeddownto1bit.

Wefurther evaluated state-of-the-art models on this benchmark forthree vision-language tasks: image captioning, visual grounding, and visual question answering. Our work aims to significantly contribute to the development ofadvanced vision-language models inthefieldofremote sensing.

Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content.