activity
20212026
most citedA Survey in Mathematical Language Processing

8 citations · 12 across the 7 of their papers we have counts for

collaborators

8 papers

cs.AI2026

FormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean

Jordan Meadows, Lan Zhang, Andre Freitas

Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specifi…

cs.CL2024★ 1 cited

Exploring the Limits of Fine-grained LLM-based Physics Inference via Premise Removal Interventions

Jordan Meadows, Tamsin James, Andre Freitas

Language models (LMs) can hallucinate when performing complex mathematical reasoning. Physics provides a rich domain for assessing their mathematical capabilities, where physical c…

cs.LG2023

Multi-Operational Mathematical Derivations in Latent Space

Marco Valentino, Jordan Meadows, Lan Zhang +1

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

cs.CL2023★ 2 cited

Controlling Equational Reasoning in Large Language Models with Prompt Interventions

Jordan Meadows, Marco Valentino, Andre Freitas

This paper investigates how hallucination rates in Large Language Models (LLMs) may be controlled via a symbolic data generation framework, exploring a fundamental relationship bet…

cs.CL2023

A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers

Jordan Meadows, Marco Valentino, Damien Teney +1

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 Transfor…

cs.CL2022★ 8 cited

A Survey in Mathematical Language Processing

Jordan Meadows, Andre Freitas

Informal mathematical text underpins real-world quantitative reasoning and communication. Developing sophisticated methods of retrieval and abstraction from this dual modality is c…

cs.CL2022

PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics

Jordan Meadows, Zili Zhou, Andre Freitas

In order for language models to aid physics research, they must first encode representations of mathematical and natural language discourse which lead to coherent explanations, wit…

cs.AI2021★ 1 cited

Similarity-Based Equational Inference in Physics

Jordan Meadows, André Freitas

Automating the derivation of published results is a challenge, in part due to the informal use of mathematics by physicists, compared to that of mathematicians. Following demand, w…