10 citations · 20 across the 14 of their papers we have counts for
5 papers · 1 filter
Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training
Jiajing Chen, Bingying Liu, Xiaoxuan Liao +3
With the rapid development of natural language processing technology, large-scale language models (LLM) have achieved remarkable results in a variety of tasks. However, how to effe…
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch
Jan Pfister, Julia Wunderle, Andreas Hotho
We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…
CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models
Shengye Wan, Cyrus Nikolaidis, Daniel Song +10
We are releasing a new suite of security benchmarks for LLMs, CYBERSECEVAL 3, to continue the conversation on empirically measuring LLM cybersecurity risks and capabilities. CYBERS…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models
Manish Bhatt, Sahana Chennabasappa, Yue Li +10
Large language models (LLMs) introduce new security risks, but there are few comprehensive evaluation suites to measure and reduce these risks. We present BenchmarkName, a novel be…