ComicVQA: A Benchmark for Visual Reasoning in Multimodal LLMs

Wed, 01 Jul 2026 00:00:00 +0000

Reasoning Robustness of LLMs to Adversarial Typographical Errors

Tue, 12 Nov 2024 00:00:00 +0000

Visualising Large Language Model Activations

Fri, 17 May 2024 00:00:00 +0000

In the field of natural language processing, large and complex neural networks, particularly transformers, have become essential due to their effectiveness in handling text data. However, despite their success, current methods for visualizing how activations of specific network units influence model outputs remain underdeveloped. This gap hinders interpretability and limits researchers’ and practitioners’ understanding of neural network behaviors during training and inference.

This project aims to address this issue by creating a robust visualizer tailored for transformer architectures. By processing training or test data, this project seeks to track and analyze changes in attention scores and other structural components of the transformer network, visualizing their evolution within individual input sentences during inference. The project involves developing innovative visualization conventions to compactly represent the high-dimensional neural network processes in a manner that is analytical and accessible, through the usage of static diagrams.

Esther Gan | Web IR NLP Group @ NUS

ComicVQA: A Benchmark for Visual Reasoning in Multimodal LLMs

Reasoning Robustness of LLMs to Adversarial Typographical Errors

Visualising Large Language Model Activations