1606, 2026

MSc Thesis presentation of Georgios Xydias – Thursday 25/06/2026

Title: “Modular LLM Pipeline for Task-Oriented Customer Service Dialogue”

Abstract:
Task-oriented dialogue (TOD) systems are a cornerstone of modern customer-service
automation, promising to handle structured user goals such as searching, booking, and
information requests through natural conversation. The recent rise of instruction-tuned Large
Language Models (LLMs) has reopened a fundamental design question: should a single
general-purpose LLM handle the entire turn, or should the responsibilities of a dialogue agent
be split into a modular pipeline of smaller, role-specific components? Existing work explores
both ends of this spectrum, but rarely compares them head-to-head under a setup that uses the
same dataset, the same evaluation procedure, and the same model families across
architectures, leaving the practical trade-offs between architectural simplicity and architectural
decomposition never brought together under a single controlled comparison.
In this work, we introduce a controlled comparative study of three task-oriented dialogue
architectures for hotel and restaurant customer service, evaluated on the MultiWOZ 2.2
benchmark. The first architecture is a single-LLM baseline that performs dialogue state tracking,
database lookup, and response generation in one prompt. The second is a zero-shot modular
pipeline that separates the dialogue state tracker from the response generator, connected by a
deterministic database lookup and a policy gate, with a lightweight guardrail on the generated
response. The third applies role-specific LoRA fine-tuning to the two LLM-based modules of the
pipeline (the dialogue state tracker, DST, and the response generator, ResponseGen) by
training small open-source models for their assigned role via QLoRA. All three architectures are
evaluated with the standardized MultiWOZ evaluator, reporting both dialogue-state metrics
(Joint Goal Accuracy, Slot F1) and end-to-end metrics (Inform, Success, BLEU, Combined),
complemented by a custom set of operational metrics covering hallucination, policy violations,
system correctness, latency, and cost.
Our findings show that a) architectural decomposition is not universally beneficial: it
substantially helps weaker models that struggle to manage every responsibility in a single
prompt, but it can hurt the strongest open-source models that already coordinate all
responsibilities reliably, and b) role-specific LoRA fine-tuning lifts dialogue-state tracking of
small open-source models close to the level of much larger commercial APIs, making them
cost-efficient, fast, and competitive alternative for slot tracking. These results show that
architectural decomposition and role-specific fine-tuning are effective tools, but only under the
right conditions, for building reliable LLM-based customer-service dialogue systems.

Date-Time: Thursday 25/06/2026 – 10:00 AM
Examination Committee:
Dr. Kosmas Kritsis
Prof. Aggelos Pikrakis
Dr. Georgios Paraskevopoulos

Msc Thesis Presentation
Thursday , June 25 · 10:00am – 11:00pm
Time zone: Europe/Athens
Microsoft Teams meeting
Join:
https://teams.microsoft.com/meet/318082679942220?p=ne3PcOyBsgIcxTjyGu

506, 2026

MSc Thesis presentation of Rafail N. Adam – Monday, 08/6/2026

Student: “Rafail N. Adam”
Program: “Data Science and Information Technologies”
Title: “MicroRNA target prediction with novel machine learning methods beyond quadratic attention”
Abstract:
MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate
gene expression, primarily by suppressing mRNA translation through
binding to MicroRNA Response Elements (MREs). They are involved in
many physiological processes, and their dysregulation is associated with
disease. Advances in multi-omics and personalized medicine have
enabled miRNA discovery and analysis through high-throughput
techniques such as small RNA-seq, AGO-eCLIP, and AGO-CLASH, which
quantify miRNA–mRNA interactions. Because a single miRNA can target
multiple mRNAs, understanding their regulatory effects requires
advanced computational approaches. Consequently, numerous machine
learning and deep learning models have been developed for miRNA
target prediction, either by identifying valid miRNA–MRE interactions or
by predicting gene-level effects using transcriptomic data. This thesis
presents the development of a machine/deep-learning model that
predicts interactions between miRNAs and MREs. AGO2-eCLIP data were
used to train and evaluate several models, resulting in a weighted
ensemble approach capable of distinguishing valid from invalid
interactions with Average Precision performance comparable to current
state-of-the-art methods. The model also provides interpretability by
estimating the contribution of each feature to the final prediction.
Date-Time: 08/06/2026 – 11:00 AM
Examination Committee:
Prof. Martin Reczko
Prof. Artemis Hatzigeorgiou
Prof. Alexandros Dimopoulos

Msc Thesis Presentation
Monday, June 8 · 11:00am – 12:00pm
Time zone: Europe/Athens
Google Meet joining info
Video call link: https://meet.google.com/wfp-snxk-trn

1805, 2026

Παρουσίαση των Μεταπτυχιακών Προγραμμάτων Σπουδών (ΠΜΣ) και Διατμηματικών Προγραμμάτων Μεταπτυχιακών Σπουδών (ΔΠΜΣ) του Τμήματος

Η Παρουσίαση των Μεταπτυχιακών Προγραμμάτων Σπουδών (ΠΜΣ) και Διατμηματικών/Διιδρυματικών Προγραμμάτων Μεταπτυχιακών Σπουδών (ΔΠΜΣ) του Τμήματος Πληροφορικής και Τηλεπικοινωνιών ΕΚΠΑ, θα πραγματοποιηθεί δια ζώσης, στο Αναγνωστήριο του Τμήματος Πληροφορικής και Τηλεπικοινωνιών την Πέμπτη 21 Μαΐου 2026, ώρα 11.00 έως 14:00.