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).

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

Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

'People thought I was a communist doing this as a non-profit': is Wikipedia's Jimmy Wales the last decent tech baron? In an online landscape characterised by doom and division, the people's encyclopedia stands out - a huge collective endeavour giving everyone free access to the sum of human knowledge. But with Elon Musk branding it'Wokipedia' and AI looming large, can it survive? W ikipedia will be 25 years old in January. Jimmy Wales's daughter will be 25 and three weeks. It's not a coincidence: on Boxing Day 2000 Wales's then wife, Christine, gave birth to a baby girl, but it quickly became clear that something wasn't right. She had breathed in contaminated amniotic fluid, resulting in a life-threatening condition called meconium aspiration syndrome. An experimental treatment was available at the hospital near where they lived in San Diego. Did they want to try it?

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.

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).

Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

In recent years, federated learning (FL) has made significant advance in privacy-sensitive applications. However, it can be hard to ensure that FL participants provide well-annotated data for training. The corresponding annotations from different clients often contain complex label noise at varying levels. This label noise issue has a substantial impact on the performance of the trained models, and clients with greater noise levels can be largely attributed for this degradation. To this end, it is necessary to develop an effective optimization strategy to alleviate the adverse effects of these noisy clients.In this study, we present a two-stage optimization framework, MaskedOptim, to address this intricate label noise problem. The first stage is designed to facilitate the detection of noisy clients with higher label noise rates. The second stage focuses on rectifying the labels of the noisy clients' data through an end-to-end label correction mechanism, aiming to mitigate the negative impacts caused by misinformation within datasets. This is achieved by learning the potential ground-truth labels of the noisy clients' datasets via backpropagation. To further enhance the training robustness, we apply the geometric median based model aggregation instead of the commonly-used vanilla averaged model aggregation. We implement sixteen related methods and conduct evaluations on three image datasets and one text dataset with diverse label noise patterns for a comprehensive comparison. Extensive experimental results indicate that our proposed framework shows its robustness in different scenarios. Additionally, our label correction framework effectively enhances the data quality of the detected noisy clients' local datasets. % Our codes will be open-sourced to facilitate related research communities. Our codes are available via https://github.com/Sprinter1999/MaskedOptim .

Although formal methods are capable of producing reliable software, they have seen minimal adoption in everyday programming. Automatic code generation using large language models is becoming increasingly widespread, but it rarely considers producing strong correctness guarantees. In this study, we explore the ability of LLMs to produce verified code in three verification languages (Dafny, Nagini, and Verus). To do so, we use manually curated datasets derived from the state-of-the-art Python benchmark, HumanEval. We also assess what types of information are sufficient to achieve good-quality results.