19 citations · 38 across the 5 of their papers we have counts for
6 papers
Dialect Normalization using Large Language Models and Morphological Rules
Antonios Dimakis, John Pavlopoulos, Antonios Anastasopoulos
Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle thi…
When Every Token Counts: Optimal Segmentation for Low-Resource Language Models
Bharath Raj, Garvit Suri, Vikrant Dewangan +1
Traditional greedy tokenization methods have been a critical step in Natural Language Processing (NLP), influencing how text is converted into tokens and directly impacting model p…
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…
Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models
Manish Bhatt, Sahana Chennabasappa, Cyrus Nikolaidis +18
This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants. As what we be…