3 citations · 8 across the 13 of their papers we have counts for
5 papers · 1 filter
LoRA-Drop: Temporal LoRA Decoding for Efficient LLM Inference
Hossein Rajabzadeh, Maryam Dialameh, Chul B. Park +2
Autoregressive large language models (LLMs) are bottlenecked by sequential decoding, where each new token typically requires executing all transformer layers. Existing dynamic-dept…
FLOP-Efficient Training: Early Stopping Based on Test-Time Compute Awareness
Hossam Amer, Maryam Dialameh, Hossein Rajabzadeh +3
Scaling training compute, measured in FLOPs, has long been shown to improve the accuracy of large language models, yet training remains resource-intensive. Prior work shows that in…
Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models
Benyamin Jamialahmadi, Parsa Kavehzadeh, Mehdi Rezagholizadeh +5
Deploying large language models (LLMs) in real-world applications is often hindered by strict computational and latency constraints. While dynamic inference offers the flexibility…
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models
Hossein Rajabzadeh, Aref Jafari, Aman Sharma +5
Large Language Models (LLMs), with their increasing depth and number of parameters, have demonstrated outstanding performance across a variety of natural language processing tasks.…
Multimodal Multi-Hop Question Answering Through a Conversation Between Tools and Efficiently Finetuned Large Language Models
Hossein Rajabzadeh, Suyuchen Wang, Hyock Ju Kwon +1
We employ a tool-interacting divide-and-conquer strategy enabling large language models (LLMs) to answer complex multimodal multi-hop questions. In particular, we harness the power…