2.3k citations · 2.4k across the 13 of their papers we have counts for
13 papers
Prompt Engineering a Prompt Engineer
Qinyuan Ye, Maxamed Axmed, Reid Pryzant +1
Prompt engineering is a challenging yet crucial task for optimizing the performance of large language models on customized tasks. It requires complex reasoning to examine the model…
Targeted Data Generation: Finding and Fixing Model Weaknesses
Zexue He, Marco Tulio Ribeiro, Fereshte Khani
Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. Addi…
Collaborative Development of NLP models
Fereshte Khani, Marco Tulio Ribeiro
Despite substantial advancements, Natural Language Processing (NLP) models often require post-training adjustments to enforce business rules, rectify undesired behavior, and align…
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani +4
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Saeid Asgari Taghanaki, Ali Gholami, Fereshte Khani +4
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy. However, we demonstrate that BN co…