Publications (5)
GneissWeb: Preparing High Quality Data for LLMs at Scale
Hajar Emami Gohari, Swanand Ravindra Kadhe, Syed Yousaf Shah +29
Data quantity and quality play a vital role in determining the performance of Large Language Models (LLMs). High-quality data, in particular, can significantly boost the LLM's abil…
Attack Techniques and Threat Identification for Vulnerabilities
Constantin Adam, Muhammed Fatih Bulut, Daby Sow +3
Modern organizations struggle with insurmountable number of vulnerabilities that are discovered and reported by their network and application vulnerability scanners. Therefore, pri…
FOLD: Fuzzy Online Deduplication for Very Large Evolving Datasets via Approximate Nearest Neighbor Search
Nelson Bore, Pritish Mishra, Constantin Adam +2
Fuzzy deduplication is key to constructing large language model training corpora. However, classic Locality-Sensitive Hashing (LSH) pipelines scale poorly as corpora grow and are i…
Partially Trusting the Service Mesh Control Plane
Constantin Adam, Abdulhamid Adebayo, Hubertus Franke +4
Zero Trust is a novel cybersecurity model that focuses on continually evaluating trust to prevent the initiation and horizontal spreading of attacks. A cloud-native Service Mesh is…
Data-Prep-Kit: getting your data ready for LLM application development
David Wood, Boris Lublinsky, Alexy Roytman +21
Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible ope…