36 citations · 43 across the 4 of their papers we have counts for
12 papers
Using Natural Language Explanations to Improve Robustness of In-context Learning
Xuanli He, Yuxiang Wu, Oana-Maria Camburu +2
Recent studies demonstrated that large language models (LLMs) can excel in many tasks via in-context learning (ICL). However, recent works show that ICL-prompted models tend to pro…
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
Maxime Kayser, Oana-Maria Camburu, Leonard Salewski +4
Recently, there has been an increasing number of efforts to introduce models capable of generating natural language explanations (NLEs) for their predictions on vision-language (VL…
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration
Lei Sha, Oana-Maria Camburu, Thomas Lukasiewicz
Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive…
The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets
Vid Kocijan, Oana-Maria Camburu, Thomas Lukasiewicz
Diagnostic datasets that can detect biased models are an important prerequisite for bias reduction within natural language processing. However, undesired patterns in the collected…
Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution
Yordan Yordanov, Oana-Maria Camburu, Vid Kocijan +1
Hard cases of pronoun resolution have been used as a long-standing benchmark for commonsense reasoning. In the recent literature, pre-trained language models have been used to obta…
Explaining Deep Neural Networks
Oana-Maria Camburu
Deep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recogn…