Large Language Model
β-DPO: Direct Preference Optimization with Dynamic β Junkang Wu
Despite the effectiveness, RLHF's instability and computational requirements often limit its practical
Scalable and Robust Speculative Decoding
As the usage of large language models (LLMs) grows, it becomes increasingly important to serve them quickly and efficiently.
Towards General Loop Invariant Generation: A Benchmark of Programs with Memory Manipulation Anonymous Author(s) Affiliation Address email 1 Overview of Supplementary Material
Dataset Documentation: We have documented our dataset for intended researchers as required. The link to download the models after fine-tuning is https://mega.nz/file/M9FEWCjD# To fill the lack of benchmarks for general loop invariant generation, we propose LIG-MM, a loop invariant generation benchmark of memory manipulation programs. Table 1 below shows the basics of the code in LIG-MM. Multiple examples are shown in Sec. 3, and the Table 1: Statistics of our proposed LIG-MM benchmark.
Towards General Loop Invariant Generation: A Benchmark of Programs with Memory Manipulation
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).
Geo-Diverse Safety Alignment Da Yin
Content W arning: This paper may contain examples of harmful contents by nature.
