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 …

We introduce a task consisting in matching a proof to a given mathematical statement. The
task fits well within current research on Mathematical Information Retrieval and, more …

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 …

The ever-growing complexity of mathematical proofs makes their manual verification by
mathematicians very cognitively demanding. Autoformalization seeks to address this by …

Abstract The Shared Task on Natural Language Premise Selection (NLPS) asks participants
to retrieve the set of premises that are most likely to be useful for proving a given …

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 …

We tackle the problem of neural machine translation of mathematical formulae between
ambiguous presentation languages and unambiguous content languages. Compared to …

Remote sensing indices are widely used in various fields of geoscience research. However,
there are limits to how effectively the knowledge of indices can be managed or analyzed …