Reto Gubelmann


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When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)
Reto Gubelmann | Aikaterini-lida Kalouli | Christina Niklaus | Siegfried Handschuh
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

In this paper, we focus on the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, as well as sentence fragments for natural language inference (NLI). On the commonly used notion of logical inference, nothing can be inferred from a question, an order, or an incomprehensible sentence fragment. We find MNLI, arguably the most important NLI dataset, and hence models fine-tuned on this dataset, insensitive to this fact. Using a symbolic semantic parser, we develop and make publicly available, fine-tuning datasets designed specifically to address this issue, with promising results. We also make a first exploration of ChatGPT’s concept of entailment.


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Context Matters: A Pragmatic Study of PLMs’ Negation Understanding
Reto Gubelmann | Siegfried Handschuh
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. So far, research in NLP on negation has almost exclusively adhered to the semantic view. In this article, we adopt the pragmatic paradigm to conduct a study of negation understanding focusing on transformer-based PLMs. Our results differ from previous, semantics-based studies and therefore help to contribute a more comprehensive – and, given the results, much more optimistic – picture of the PLMs’ negation understanding.

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A Philosophically-Informed Contribution to the Generalization Problem of Neural Natural Language Inference: Shallow Heuristics, Bias, and the Varieties of Inference
Reto Gubelmann | Christina Niklaus | Siegfried Handschuh
Proceedings of the 3rd Natural Logic Meets Machine Learning Workshop (NALOMA III)

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On What it Means to Pay Your Fair Share: Towards Automatically Mapping Different Conceptions of Tax Justice in Legal Research Literature
Reto Gubelmann | Peter Hongler | Elina Margadant | Siegfried Handschuh
Proceedings of the Natural Legal Language Processing Workshop 2022

In this article, we explore the potential and challenges of applying transformer-based pre-trained language models (PLMs) and statistical methods to a particularly challenging, yet highly important and largely uncharted domain: normative discussions in tax law research. On our conviction, the role of NLP in this essentially contested territory is to make explicit implicit normative assumptions, and to foster debates across ideological divides. To this goal, we propose the first steps towards a method that automatically labels normative statements in tax law research, and that suggests the normative background of these statements. Our results are encouraging, but it is clear that there is still room for improvement.