/research
Research
My academic work sits at the intersection of natural language processing, multilingual alignment, and model interpretability. I am particularly interested in understanding what small language models learn about language structure across typologically different languages — and whether that learning is genuinely cross-lingual or a surface-level statistical artefact.
ResearchGate ↗Interests
Cross-lingual NLP
How models transfer knowledge across languages and what internal representations make this possible
LLM Interpretability
Probing internal model representations to understand what language models actually learn — and what they don't
Multilingual Alignment
Aligning semantic spaces across typologically diverse languages without losing cultural and pragmatic nuance
On-device AI
Efficient model compression, quantisation, and deployment strategies for real-world edge constraints
Behavioural Signal Processing
Using passive sensing and physiological data to model human wellbeing and detect behavioural patterns
Papers
Interpretable Cross-Lingual Alignment in Small Language Models: Probing Cultural and Pragmatic Reasoning in Japanese-English Bilingual LLMs
Investigates how small Japanese language models represent and handle pragmatic meaning, focusing on areas where general-purpose LLMs often fail Japanese users. Addresses two major gaps: the lack of fine-grained evaluation methods for Japanese small language models beyond broad benchmarks such as JGLUE, and the limited understanding of culturally and pragmatically sensitive phenomena — honorifics, in-group vs. out-group reference, zero anaphora, and indirect refusal. Introduces J-PragEval-v0, a minimal-pair benchmark isolating four core pragmatic phenomena from surface fluency. Using linear probing and teacher-forced log-probability analysis, the study examines how these distinctions are encoded inside TinySwallow-1.5B. Proposes Pragmatic Representation Steering (PRS), a parameter-free inference-time method for steering model behaviour by editing residual-stream activations.
Key Contributions
- ▸J-PragEval-v0 — minimal-pair benchmark for four core Japanese pragmatic phenomena
- ▸Probing study of TinySwallow-1.5B: honorific register strongly encoded in residual stream; in-group/out-group and zero anaphora resolved dynamically at generation time
- ▸Pragmatic Representation Steering (PRS) — parameter-free inference-time steering via residual-stream activation editing
The Foundational Role of Data Structures and Algorithms in Artificial Intelligence Systems
Artificial Intelligence has transformed from a theoretical discipline to a cornerstone of modern technology, powering applications from autonomous vehicles to personalised medicine. At the heart of every AI system lies a carefully orchestrated interplay between data structures and algorithms that determines efficiency, scalability, and capability. Through theoretical analysis and practical case studies, this thesis demonstrates that the selection of appropriate data structures and algorithmic approaches directly impacts AI performance across search, optimisation, machine learning, and knowledge representation. Traces the historical evolution from early symbolic AI relying on tree structures and graph search to modern deep learning architectures utilising tensors and backpropagation — and argues that continued innovation in algorithms remains essential for addressing current AI challenges.
Key Contributions
- ▸Systematic mapping of classical algorithms (A*, dynamic programming, graph search) to modern ML training and inference
- ▸Historical analysis tracing symbolic AI through to transformer architectures via algorithmic lineage
- ▸Concrete examples of how data structure choices affect memory consumption, processing speed, and scalability in real-world AI systems