Generation and Comprehension of Unambiguous Object Descriptions. - Junhua Mao; Jonathan Huang; Alexander Toshev; Oana Camburu; Alan L. Yuille; Kevin Murphy 002 (CVPR, 2016)

e-SNLI: Natural Language Inference with Natural Language Explanations. - Oana-Maria Camburu; Tim Rocktäschel; Thomas Lukasiewicz; Phil Blunsom (NeurIPS, 2018)

A Surprisingly Robust Trick for the Winograd Schema Challenge. - Vid Kocijan; Ana-Maria Cretu 002; Oana-Maria Camburu; Yordan Yordanov; Thomas Lukasiewicz (ACL (1), 2019)

WikiCREM: A Large Unsupervised Corpus for Coreference Resolution. - Vid Kocijan; Oana-Maria Camburu; Ana-Maria Cretu 002; Yordan Yordanov; Phil Blunsom; Thomas Lukasiewicz (EMNLP/IJCNLP (1), 2019)

Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods. - Oana-Maria Camburu; Eleonora Giunchiglia; Jakob N. Foerster; Thomas Lukasiewicz; Phil Blunsom (CoRR, 2019)

Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations. - Oana-Maria Camburu; Brendan Shillingford; Pasquale Minervini; Thomas Lukasiewicz; Phil Blunsom (ACL, 2020)

Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution. - Yordan Yordanov; Oana-Maria Camburu; Vid Kocijan; Thomas Lukasiewicz (EMNLP (1), 2020)

e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations. - Virginie Do; Oana-Maria Camburu; Zeynep Akata; Thomas Lukasiewicz (CoRR, 2020)

The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets. - Oana-Maria Camburu; Eleonora Giunchiglia; Jakob N. Foerster; Thomas Lukasiewicz; Phil Blunsom (CoRR, 2020)

Explaining Deep Neural Networks. - Oana-Maria Camburu (CoRR, 2020)

Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration. - Lei Sha; Oana-Maria Camburu; Thomas Lukasiewicz (CoRR, 2020)

Explaining deep neural networks. - Oana-Maria Camburu (2020)

The Gap on Gap: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets. - Vid Kocijan; Oana-Maria Camburu; Thomas Lukasiewicz (AAAI, 2021)

Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration. - Lei Sha; Oana-Maria Camburu; Thomas Lukasiewicz (AAAI, 2021)

Towards Explainable and Trustworthy Autonomous Physical Systems. - Daniel Omeiza; Sule Anjomshoae; Konrad Kollnig; Oana-Maria Camburu; Kary Främling; Lars Kunze (CHI Extended Abstracts, 2021)

e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks. - Maxime Kayser; Oana-Maria Camburu; Leonard Salewski; Cornelius Emde; Virginie Do; Zeynep Akata; Thomas Lukasiewicz (ICCV, 2021)

Rationale-Inspired Natural Language Explanations with Commonsense. - Bodhisattwa Prasad Majumder; Oana-Maria Camburu; Thomas Lukasiewicz; Julian J. McAuley (CoRR, 2021)

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations. - Yordan Yordanov; Vid Kocijan; Thomas Lukasiewicz; Oana-Maria Camburu (CoRR, 2021)

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup. - Yordan Yordanov; Vid Kocijan; Thomas Lukasiewicz; Oana-Maria Camburu (EMNLP (Findings), 2022)

Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations. - Bodhisattwa Prasad Majumder; Oana Camburu; Thomas Lukasiewicz; Julian J. McAuley (ICML, 2022)

Explaining Chest X-Ray Pathologies in Natural Language. - Maxime Kayser; Cornelius Emde; Oana-Maria Camburu; Guy Parsons; Bartlomiej W. Papiez; Thomas Lukasiewicz (MICCAI (5), 2022)

Faithfulness Tests for Natural Language Explanations. - Pepa Atanasova; Oana-Maria Camburu; Christina Lioma; Thomas Lukasiewicz; Jakob Grue Simonsen; Isabelle Augenstein (ACL (2), 2023)

KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations. - Myeongjun Jang; Bodhisattwa Prasad Majumder; Julian J. McAuley; Thomas Lukasiewicz; Oana-Maria Camburu (ACL (2), 2023)

Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns. - Zhongbin Xie; Vid Kocijan; Thomas Lukasiewicz; Oana-Maria Camburu (EACL, 2023)

Rationalizing predictions by adversarial information calibration. - Lei Sha; Oana-Maria Camburu; Thomas Lukasiewicz (Artif. Intell., 2023)

Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms. - Joe Stacey; Pasquale Minervini; Haim Dubossarsky; Oana-Maria Camburu; Marek Rei (CoRR, 2023)

SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations. - Jesus Solano; Oana-Maria Camburu; Pasquale Minervini (CoRR, 2023)

Using Natural Language Explanations to Improve Robustness of In-context Learning for Natural Language Inference. - Xuanli He; Yuxiang Wu; Oana-Maria Camburu; Pasquale Minervini; Pontus Stenetorp (CoRR, 2023)

Using Natural Language Explanations to Improve Robustness of In-context Learning. - Xuanli He; Yuxiang Wu; Oana-Maria Camburu; Pasquale Minervini; Pontus Stenetorp (ACL (1), 2024)

The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models. - Noah Y. Siegel; Oana-Maria Camburu; Nicolas Heess; María Pérez-Ortiz 001 (ACL (Short Papers), 2024)

SparseFit: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations. - Jesus Solano; Mardhiyah Sanni; Oana-Maria Camburu; Pasquale Minervini (ACL (1), 2024)

Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting. - Maxime Kayser; Bayar Menzat; Cornelius Emde; Bogdan Bercean; Alex Novak; Abdalá Morgado; Bartlomiej W. Papiez; Susanne Gaube; Thomas Lukasiewicz; Oana-Maria Camburu (EMNLP, 2024)

Atomic Inference for NLI with Generated Facts as Atoms. - Joe Stacey; Pasquale Minervini; Haim Dubossarsky; Oana-Maria Camburu; Marek Rei (EMNLP, 2024)

Identifying Linear Relational Concepts in Large Language Models. - David Chanin; Anthony Hunter; Oana-Maria Camburu (NAACL-HLT, 2024)

From Explanations to Action: A Zero-Shot, Theory-Driven LLM Framework for Student Performance Feedback. - Vinitra Swamy; Davide Romano; Bhargav Srinivasa Desikan; Oana-Maria Camburu; Tanja Käser (CoRR, 2024)

Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting. - Maxime Kayser; Bayar Menzat; Cornelius Emde; Bogdan Bercean; Alex Novak; Abdala Espinosa; Bartlomiej W. Papiez; Susanne Gaube; Thomas Lukasiewicz; Oana-Maria Camburu (CoRR, 2024)

iLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback. - Vinitra Swamy; Davide Romano; Bhargav Srinivasa Desikan; Oana-Maria Camburu; Tanja Käser (AAAI, 2025)

AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction. - Magnus Sesodia; Alina Petrova; John Armour; Thomas Lukasiewicz; Oana-Maria Camburu; Puneet K. Dokania; Philip Torr 001; Christian Schröder de Witt (CoRR, 2025)

Faithfulness of LLM Self-Explanations for Commonsense Tasks: Larger Is Better, and Instruction-Tuning Allows Trade-Offs but Not Pareto Dominance. - Noah Y. Siegel; Nicolas Heess; María Pérez-Ortiz 001; Oana-Maria Camburu (CoRR, 2025)