8 citations · 12 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…