activity
20222026
most citedLAR-ECHR: A New Legal Argument Reasoning Task and Dataset for Cases of the European Court of Human Rights

1 citations · 2 across the 8 of their papers we have counts for

collaborators

8 papers

cs.IR2026

GreekBarRetrieval: A Benchmark for Greek Statutory Retrieval

Ernest Beta, Odysseas S. Chlapanis, Dimitrios Galanis +1

Statutory retrieval is necessary for citation-grounded legal question answering, but remains underexplored for Greek. We introduce GreekBarRetrieval, a public retrieval benchmark d…

cs.CL2026

The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans

Odysseas S. Chlapanis, Orfeas Menis Mastromichalakis, Christos H. Papadimitriou

Abstract concepts - justice, theory, availability - have no single perceivable referent; in the human brain, their meaning emerges from a web of experiences, affect, and social con…

cs.CL2025

GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations

Odysseas S. Chlapanis, Dimitrios Galanis, Nikolaos Aletras +1

We introduce GreekBarBench, a benchmark that evaluates LLMs on legal questions across five different legal areas from the Greek Bar exams, requiring citations to statutory articles…

cs.CL2024

AUEB-Archimedes at RIRAG-2025: Is obligation concatenation really all you need?

Ioannis Chasandras, Odysseas S. Chlapanis, Ion Androutsopoulos

This paper presents the systems we developed for RIRAG-2025, a shared task that requires answering regulatory questions by retrieving relevant passages. The generated answers are e…

cs.CL2024★ 1 cited

LAR-ECHR: A New Legal Argument Reasoning Task and Dataset for Cases of the European Court of Human Rights

Odysseas S. Chlapanis, Dimitrios Galanis, Ion Androutsopoulos

We present Legal Argument Reasoning (LAR), a novel task designed to evaluate the legal reasoning capabilities of Large Language Models (LLMs). The task requires selecting the corre…

cs.CL2024★ 1 cited

Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs

Christos Fragkathoulas, Odysseas S. Chlapanis

This paper introduces a novel task to assess the faithfulness of large language models (LLMs) using local perturbations and self-explanations. Many LLMs often require additional co…