55 citations · 81 across the 9 of their papers we have counts for
6 papers · 1 filter
Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity
Amir Joudaki, Giulia Lanzillotta, Mohammad Samragh Razlighi +5
Deep learning models excel in stationary data but struggle in non-stationary environments due to a phenomenon known as loss of plasticity (LoP), the degradation of their ability to…
Computational Bottlenecks of Training Small-scale Large Language Models
Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh +4
While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. Howe…
SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF
Atoosa Chegini, Hamid Kazemi, Iman Mirzadeh +5
In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally re…
Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models
Keivan Alizadeh, Iman Mirzadeh, Hooman Shahrokhi +6
Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recen…
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi +3
Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to…
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
Iman Mirzadeh, Keivan Alizadeh, Sachin Mehta +5
Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant…