Automating discovery in mathematics and science will require sophisticated methods of
information extraction and abstract reasoning, including models that can convincingly …

The derivation of mathematical results in specialised fields using Large Language Models
(LLMs) is an emerging research direction that can help identify models' limitations, and …

This paper investigates the possibility of approximating multiple mathematical operations in
latent space for expression derivation. To this end, we introduce different multi-operational …

Whether Transformers can learn to apply symbolic rules and generalise to out-of-distribution
examples is an open research question. In this paper, we devise a data generation method …

Abstract Natural Language Premise Selection (NLPS) is a mathematical Natural Language
Processing (NLP) task that retrieves a set of applicable relevant premises to support the end …

Automated theorem proving can benefit a lot from methods employed in natural language
processing, knowledge graphs and information retrieval: this non-trivial task combines …

Language models can hallucinate when performing complex and detailed mathematical
reasoning. Physics provides a rich domain for assessing mathematical reasoning …

This paper provides an overview on graph databases for the retrieval and the integration of
knowledge originating from textual data, attempting to bring together different bricks that are …

This paper proposes a methodology for generating and perturbing detailed derivations of
equations at scale, aided by a symbolic engine, to evaluate the generalisability of …

This paper provides an overview on graph databases for the retrieval and the integration of
knowledge originating from textual data, attempting to bring together different bricks that are …