30 citations · 48 across the 28 of their papers we have counts for
6 papers · 1 filter
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
Tao Yang, Jinghao Deng, Xiaojun Quan +2
Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available. While dropout proves to be an effective…
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Nan Wang, Qifan Wang, Yi-Chia Wang +5
As language models become increasingly integrated into our digital lives, Personalized Text Generation (PTG) has emerged as a pivotal component with a wide range of applications. H…
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Aaron Chan, Shaoliang Nie, Liang Tan +4
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior. However, free-text rationales' unconstrained natu…
ER-Test: Evaluating Explanation Regularization Methods for Language Models
Brihi Joshi, Aaron Chan, Ziyi Liu +4
By explaining how humans would solve a given task, human rationales can provide strong learning signal for neural language models (LMs). Explanation regularization (ER) aims to imp…
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
Khalil Mrini, Shaoliang Nie, Jiatao Gu +3
We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties addre…
BARACK: Partially Supervised Group Robustness With Guarantees
Nimit S. Sohoni, Maziar Sanjabi, Nicolas Ballas +4
While neural networks have shown remarkable success on classification tasks in terms of average-case performance, they often fail to perform well on certain groups of the data. Suc…