From data to model behaviour: A causal and empirical analysis of how data shapes the behaviour of language models
type: CreativeWork
description: Large language models have achieved remarkable capabilities, yet their behaviour remains difficult to predict and understand. A central challenge is that these models are shaped not only by their architectures or training algorithms, but fundamentally by their training data---what examples they see, how those examples are represented, when they encounter them, and how much influence individual examples (or batches of examples) exert. Despite the critical role of training data, a systematic understanding of how data-centric decisions propagate through the training pipeline to affect final model behaviour has remained limited. This thesis addresses this gap by examining four stages of the data pipeline: data sourcing and curation, data representation, model training, and model evaluation. The thesis makes four primary contributions, each addressing a distinct stage. First, I introduce AnchorAL, a novel Active Learning approach that addresses the computational and learning challenges of data sourcing and curation. The method maintains constant computational costs whilst discovering more minority class examples and (often) achieving higher performance than previous comparable approaches. Second, I develop a causal framework using the Regression Discontinuity Design to measure tokenisation bias---how vocabulary choices causally affect probability assignments. The analysis reveals that character spans represented as single tokens receive up to 17 times more probability than when split, with effects persisting even in larger models. Third, through PolyPythia---a suite of 45 new training runs spanning 5 model sizes---I characterise training stability across randomness factors. The analysis identifies remarkably consistent learning phases across seeds and scales, whilst revealing that smaller models can predict larger models' learning dynamics. Fourth, I develop a causal framework using the Difference-in-Differences methodology to measure memorisation as the causal effect of…
abstract: Large language models have achieved remarkable capabilities, yet their behaviour remains difficult to predict and understand. A central challenge is that these models are shaped not only by their architectures or training algorithms, but fundamentally by their training data---what examples they see, how those examples are represented, when they encounter them, and how much influence individual examples (or batches of examples) exert. Despite the critical role of training data, a systematic understanding of how data-centric decisions propagate through the training pipeline to affect final model behaviour has remained limited. This thesis addresses this gap by examining four stages of the data pipeline: data sourcing and curation, data representation, model training, and model evaluation. The thesis makes four primary contributions, each addressing a distinct stage. First, I introduce AnchorAL, a novel Active Learning approach that addresses the computational and learning challenges of data sourcing and curation. The method maintains constant computational costs whilst discovering more minority class examples and (often) achieving higher performance than previous comparable approaches. Second, I develop a causal framework using the Regression Discontinuity Design to measure tokenisation bias---how vocabulary choices causally affect probability assignments. The analysis reveals that character spans represented as single tokens receive up to 17 times more probability than when split, with effects persisting even in larger models. Third, through PolyPythia---a suite of 45 new training runs spanning 5 model sizes---I characterise training stability across randomness factors. The analysis identifies remarkably consistent learning phases across seeds and scales, whilst revealing that smaller models can predict larger models' learning dynamics. Fourth, I develop a causal framework using the Difference-in-Differences methodology to measure memorisation as the causal effect of…
author: Lesci, Pietro
language: eng
keywords: Natural Language Processing, Causal Analysis, Machine Learning, Language Models, Econometrics
license: https://www.repository.cam.ac.uk/bitstreams/dbb0d6cf-9cb0-4079-b04c-edd50b217868/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2025-12-29
date modified: 2026-07-24T01:33:00Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.131697