33 citations · 35 across the 3 of their papers we have counts for
3 papers
Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation
Aeree Cho, Grace C. Kim, Alexander Karpekov +6
The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, its complexity makes it difficult for no…
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
ShengYun Peng, Aishwarya Chakravarthy, Seongmin Lee +3
Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table recognition (TR) has m…
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked
Mansi Phute, Alec Helbling, Matthew Hull +4
Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning. Adver…