51 citations · 61 across the 5 of their papers we have counts for
5 papers
Sink-Aware Pruning for Diffusion Language Models
Aidar Myrzakhan, Tianyi Li, Bowei Guo +2
Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressi…
DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
Jennifer Chen, Aidar Myrzakhan, Yaxin Luo +3
Retrieval-Augmented Generation (RAG) methods have proven highly effective for tasks requiring factual consistency and robust knowledge retrieval. However, large-scale RAG systems c…
Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmark
Sondos Mahmoud Bsharat, Mukul Ranjan, Aidar Myrzakhan +6
Rapid advancements in large language models (LLMs) have increased interest in deploying them on mobile devices for on-device AI applications. Mobile users interact differently with…
Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena
Aidar Myrzakhan, Sondos Mahmoud Bsharat, Zhiqiang Shen
Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs). Typically, an LLM is given a question and selects the answer deemed most probable after…
Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Sondos Mahmoud Bsharat, Aidar Myrzakhan, Zhiqiang Shen
This paper introduces 26 guiding principles designed to streamline the process of querying and prompting large language models. Our goal is to simplify the underlying concepts of f…