The integration of synthetically generated data in the second and third steps enhances the models' capabilities in interpreting and processing legal texts, effectively reaching state-of-the-art performance and outperforming

This makes them overlook the many possible interactions across the input modalities and deems them unsafe for high-risk tasks requiring reliable uncertainty estimation.

We collect 312 programs from various sources, including daily programs from college homework, the international competition (SV -COMP), benchmarks from previous papers (SLING), and programs from real-world software systems (Linux Kernel, GlibC, LiteOS, and Zephyr).

This restricts the tokenizer's ability to effectively leverage the redundancy

Despite significant progress inthisarea, anessential factor often remains overlooked:geo-diversity. Recognizing and incorporating geographical variations [41, 40, 4, 10, 31, 6] in safety principles is crucial in the global landscape of LLM safety. Cultural norms and legal frameworks vary widely, resulting in diverse definitions of safe and acceptable behavior. As shown in Figure 1, while giving a green hatasagift might bebenign inmanycultures, itisconsidered offensiveinChina.

With the rapid development of code LLMs, many popular evaluation benchmarks, such as HumanEval, DS-1000, and MBPP, have emerged to measure the performance of code LLMs with a particular focus on code generation tasks. However, they are insufficient to cover the full range of expected capabilities of code LLMs, which span beyond code generation to answering diverse coding-related questions.

We build our dataset from triangu-lated meshes derived from Objaverse-XL and Thingi10K datasets.