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Ricardo Dominguez-Olmedo

7 accepted papers

2025

Lawma: The Power of Specialization for Legal Annotation

ICLR 2025poster

Annotation and classification of legal text are central components of empirical legal research. Traditionally, these tasks are often delegated to trained research assistants. Motivated by the advances in language modeling, empirical legal scholars are increasingly turning to commercial models, hopin…

2025

Training on the Test Task Confounds Evaluation and Emergence

ICLR 2025oral

We study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not a malpractice. Rather, the term describes a growing set of tec…

2024

Questioning the Survey Responses of Large Language Models

NeurIPS 2024oral

Surveys have recently gained popularity as a tool to study large language models. By comparing models’ survey responses to those of different human reference populations, researchers aim to infer the demographics, political opinions, or values best represented by current language models. In this wor…

2023

On Data Manifolds Entailed by Structural Causal Models

ICML 2023poster

The geometric structure of data is an important inductive bias in machine learning. In this work, we characterize the data manifolds entailed by structural causal models. The strengths of the proposed framework are twofold: firstly, the geometric structure of the data manifolds is causally informed,…

Cited by 6SourcePDFScholar
2022

On the Adversarial Robustness of Causal Algorithmic Recourse

ICML 2022spotlight

Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable classification outcomes from automated decision-making systems. Recourse recommendations should ideally be robust to reasonably small uncertainty in the features of the individual seeking recours…