Yousef El-kurdi — accepted AI-conference papers

2024

INDUS: Effective and Efficient Language Models for Scientific Applications

Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung +30

EMNLP 2024industry

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this insight, we developed INDUS, a…

Cited by 8 Source ↗PDF ↗Scholar ↗

2023

Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs

Young-Suk Lee, Md Arafat Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian +2

EMNLP 2023long findings

Using in-context learning (ICL) for data generation, techniques such as Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023) can train strong conversational agents with only a small amount of human supervision. One limitation of these approaches is that they resort to very…

Cited by 0 Source ↗Scholar ↗

2022

Zero-Shot Dynamic Quantization for Transformer Inference

Yousef El-kurdi, Jerry Quinn, Avi Sil

EMNLP 2022industry

We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing models either modify the training procedure, or they require an additional calibration step to adjust parameters that also…

Cited by 2 Source ↗PDF ↗Scholar ↗

2020

Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers

Yousef El-Kurdi, Hiroshi Kanayama, Efsun Sarioglu Kayi, Vittorio Castelli, Todd Ward, Radu Florian

COLING 2020industry

We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text. We introduce a data augmentation technique that uses synthetic treebanks to improve produc…

Cited by 3 Source ↗PDF ↗Scholar ↗