565 citations · 571 across the 4 of their papers we have counts for
5 papers
How much do LLMs learn from negative examples?
Shadi Hamdan, Deniz Yuret
Large language models (LLMs) undergo a three-phase training process: unsupervised pre-training, supervised fine-tuning (SFT), and learning from human feedback (RLHF/DPO). Notably,…
CarFormer: Self-Driving with Learned Object-Centric Representations
Shadi Hamdan, Fatma Güney
The choice of representation plays a key role in self-driving. Bird's eye view (BEV) representations have shown remarkable performance in recent years. In this paper, we propose to…
Self-Supervised Learning with an Information Maximization Criterion
Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik +2
Self-supervised learning allows AI systems to learn effective representations from large amounts of data using tasks that do not require costly labeling. Mode collapse, i.e., the m…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
A containerized proof-of-concept implementation of LightChain system
Yahya Hassanzadeh-Nazarabadi, Nazir Nayal, Shadi Sameh Hamdan +2
LightChain is the first Distributed Hash Table (DHT)-based blockchain with a logarithmic asymptotic message and memory complexity. In this demo paper, we present the software archi…