25 citations · 30 across the 11 of their papers we have counts for
11 papers
CASSANDRA: Programmatic and Probabilistic Learning and Inference for Stochastic World Modeling
Panagiotis Lymperopoulos, Abhiramon Rajasekharan, Ian Berlot-Attwell +2
Building world models is essential for planning in real-world domains such as businesses. Since such domains have rich semantics, we can leverage world knowledge to effectively mod…
Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions
Stéphane Aroca-Ouellette, Ian Berlot-Attwell, Panagiotis Lymperopoulos +5
Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making. Practical domains such as bus…
A Compute-Matched Re-Evaluation of TroVE on MATH
Tobias Sesterhenn, Ian Berlot-Attwell, Janis Zenkner +1
Reusing established theorems and formulas is central to mathematical problem solving, serving as essential building blocks for tackling increasingly complex challenges. Recent work…
LLM Library Learning Fails: A LEGO-Prover Case Study
Ian Berlot-Attwell, Frank Rudzicz, Xujie Si
Recent advancements in the coding, reasoning, and tool-using abilities of LLMs have spurred interest in library learning (i.e., online learning through the creation, storage, and r…
Library Learning Doesn't: The Curious Case of the Single-Use "Library"
Ian Berlot-Attwell, Frank Rudzicz, Xujie Si
Advances in Large Language Models (LLMs) have spurred a wave of LLM library learning systems for mathematical reasoning. These systems aim to learn a reusable library of tools, suc…
Attribute Diversity Determines the Systematicity Gap in VQA
Ian Berlot-Attwell, Kumar Krishna Agrawal, A. Michael Carrell +2
Although modern neural networks often generalize to new combinations of familiar concepts, the conditions that enable such compositionality have long been an open question. In this…
Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple Metric
Ian Berlot-Attwell, Frank Rudzicz
In this work, we evaluate various existing dialogue relevance metrics, find strong dependency on the dataset, often with poor correlation with human scores of relevance, and propos…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata
Ian Berlot-Attwell
An ultimate goal of the AI and ML fields is artificial general intelligence (AGI); although such systems remain science fiction, various models exhibit aspects of AGI. In this work…
On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems
Ian Berlot-Attwell, Frank Rudzicz
Automatically evaluating text-based, non-task-oriented dialogue systems (i.e., `chatbots') remains an open problem. Previous approaches have suffered challenges ranging from poor c…
Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP
John Chen, Ian Berlot-Attwell, Safwan Hossain +2
Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as freetext. We propose a novel task of exploring fai…