7 papers · 1 filter
AILS-NTUA at SemEval-2026 Task 8: Evaluating Multi-Turn RAG Conversations
Dimosthenis Athanasiou, Maria Lymperaiou, Giorgos Filandrianos +2
We present the AILS-NTUA system for SemEval-2026 Task 8 (MTRAGEval), addressing all three subtasks of multi-turn retrieval-augmented generation: passage retrieval (A), reference-gr…
AILS-NTUA at SemEval-2026 Task 3: Efficient Dimensional Aspect-Based Sentiment Analysis
Stavros Gazetas, Giorgos Filandrianos, Maria Lymperaiou +3
In this paper, we present AILS-NTUA system for Track-A of SemEval-2026 Task 3 on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which encompasses three complementary proble…
AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection
Panagiotis Alexios Spanakis, Maria Lymperaiou, Giorgos Filandrianos +2
This paper presents a novel agentic LLM pipeline for SemEval-2026 Task 10 that jointly extracts psycholinguistic conspiracy markers and detects conspiracy endorsement. Unlike tradi…
AILS-NTUA at SemEval-2026 Task 12: Graph-Based Retrieval and Reflective Prompting for Abductive Event Reasoning
Nikolas Karafyllis, Maria Lymperaiou, Giorgos Filandrianos +2
We present a winning three-stage system for SemEval 2026 Task~12: Abductive Event Reasoning that combines graph-based retrieval, LLM-driven abductive reasoning with prompt design o…
AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question Answering
Andreas Evangelatos, Giorgos Filandrianos, Maria Lymperaiou +2
In this paper, we present our submission to SemEval-2025 Task 8: Question Answering over Tabular Data. This task, evaluated on the DataBench dataset, assesses Large Language Models…
AILS-NTUA at SemEval-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunking
Iraklis Premptis, Maria Lymperaiou, Giorgos Filandrianos +3
The Unlearning Sensitive Content from Large Language Models task aims to remove targeted datapoints from trained models while minimally affecting their general knowledge. In our wo…