277 citations · 596 across the 15 of their papers we have counts for
17 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…
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations
Jesus Solano, Mardhiyah Sanni, Oana-Maria Camburu +1
Models that generate natural language explanations (NLEs) for their predictions have recently gained increasing interest. However, this approach usually demands large datasets of h…
Atomic Inference for NLI with Generated Facts as Atoms
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +2
With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models…
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…
The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets
Oana-Maria Camburu, Eleonora Giunchiglia, Jakob Foerster +2
For neural models to garner widespread public trust and ensure fairness, we must have human-intelligible explanations for their predictions. Recently, an increasing number of works…
e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations
Virginie Do, Oana-Maria Camburu, Zeynep Akata +1
The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning. However, the automatic way in w…
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini +2
To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for the…
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods
Oana-Maria Camburu, Eleonora Giunchiglia, Jakob Foerster +2
For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks. In this work, we ide…
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
Vid Kocijan, Oana-Maria Camburu, Ana-Maria Cretu +3
Pronoun resolution is a major area of natural language understanding. However, large-scale training sets are still scarce, since manually labelling data is costly. In this work, we…
A Surprisingly Robust Trick for Winograd Schema Challenge
Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu +2
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this p…
e-SNLI: Natural Language Inference with Natural Language Explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz +1
In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from…
Generation and Comprehension of Unambiguous Object Descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev +3
We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or inte…
Cyclotomic coefficients: gaps and jumps
Oana-Maria Camburu, Emil-Alexandru Ciolan, Florian Luca +2
We improve several recent results by Hong, Lee, Lee and Park (2012) on gaps and Bzdȩga (2014) on jumps amongst the coefficients of cyclotomic polynomials. Besides direct improvemen…