4 papers
Robustness of Neurosymbolic Reasoners on First-Order Logic Problems
Hannah Bansal, Kemal Kurniawan, Lea Frermann
Recent trends in NLP aim to improve reasoning capabilities in Large Language Models (LLMs), with key focus on generalization and robustness to variations in tasks. Counterfactual t…
Training and Evaluating with Human Label Variation: An Empirical Study
Kemal Kurniawan, Meladel Mistica, Timothy Baldwin +1
Human label variation (HLV) challenges the standard assumption that a labelled instance has a single ground truth, instead embracing the natural variation in human annotation to tr…
MoDEM: Mixture of Domain Expert Models
Toby Simonds, Kemal Kurniawan, Jey Han Lau
We propose a novel approach to enhancing the performance and efficiency of large language models (LLMs) by combining domain prompt routing with domain-specialized models. We introd…
To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction
Kemal Kurniawan, Meladel Mistica, Timothy Baldwin +1
This paper explores the task of automatic prediction of text spans in a legal problem description that support a legal area label. We use a corpus of problem descriptions written b…