View publication

Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant challenges for deployment on resource-constrained devices. Despite recent trends favoring alternative activation functions such as GELU or SiLU, known for increased computation, this study strongly advocates for reinstating ReLU activation in LLMs. We demonstrate that using the ReLU activation function has a negligible impact on convergence and performance while significantly reducing computation and weight transfer. This reduction is particularly valuable during the memory-bound inference step, where efficiency is paramount. Exploring sparsity patterns in ReLU-based LLMs, we unveil the reutilization of activated neurons for generating new tokens and leveraging these insights we propose practical strategies to substantially reduce LLM inference computation up to three times, using ReLU activations with minimal performance trade-offs.

Related readings and updates.

DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

*=Equal Contributors Controversy is a reflection of our zeitgeist and an important aspect of any discourse. The rise of large language models (LLMs) as conversational systems has increased public reliance on these systems for answers to their various questions. Consequently, it is crucial to systematically examine how these models respond to questions that pertain to ongoing debates. However, few such datasets exist in providing human-annotated…
See paper details

Gender Bias in LLMs

Large Language Models (LLMs) have made substantial progress in the past several months, shattering state-of-the-art benchmarks in many domains. This paper investigates LLMs' behavior with respect to gender stereotypes, a known stumbling block for prior models. We propose a simple paradigm to test the presence of gender bias, building on but differing from WinoBias, a commonly used gender bias dataset which is likely to be included in the training…
See paper details