18 citations · 18 across the 5 of their papers we have counts for
7 papers
Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models
Xingang Guo, Jing Gu, Brian Jang +26
Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek determines if a vehicle can fit bet…
Dynamic Subset Tuning: Expanding the Operational Range of Parameter-Efficient Training for Large Language Models
Felix Stahlberg, Jared Lichtarge, Shankar Kumar
We propose a novel parameter-efficient training (PET) method for large language models that adapts models to downstream tasks by optimizing a small subset of the existing model par…
Heterogeneous Federated Learning Using Knowledge Codistillation
Jared Lichtarge, Ehsan Amid, Shankar Kumar +3
Federated Averaging, and many federated learning algorithm variants which build upon it, have a limitation: all clients must share the same model architecture. This results in unus…
Simple and Effective Gradient-Based Tuning of Sequence-to-Sequence Models
Jared Lichtarge, Chris Alberti, Shankar Kumar
Recent trends towards training ever-larger language models have substantially improved machine learning performance across linguistic tasks. However, the huge cost of training larg…
Data Weighted Training Strategies for Grammatical Error Correction
Jared Lichtarge, Chris Alberti, Shankar Kumar
Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining d…
Corpora Generation for Grammatical Error Correction
Jared Lichtarge, Chris Alberti, Shankar Kumar +3
Grammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, G…
Weakly Supervised Grammatical Error Correction using Iterative Decoding
Jared Lichtarge, Christopher Alberti, Shankar Kumar +2
We describe an approach to Grammatical Error Correction (GEC) that is effective at making use of models trained on large amounts of weakly supervised bitext. We train the Transform…