1 citations · 2 across the 7 of their papers we have counts for
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The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques
Sebastian Ochs, Ivan Habernal
Removing personally identifiable information (PII) from texts is necessary to comply with various data protection regulations and to enable data sharing without compromising privac…
Legal Experts Disagree With Rationale Extraction Techniques for Explaining ECtHR Case Outcome Classification
Mahammad Namazov, Tomáš Koref, Ivan Habernal
Interpretability is critical for applications of large language models (LLMs) in the legal domain, where trust and transparency are essential. A central NLP task in this setting is…
Mining Legal Arguments to Study Judicial Formalism
Tomáš Koref, Lena Held, Mahammad Namazov +3
Courts must justify their decisions, but systematically analyzing judicial reasoning at scale remains difficult. This study tests claims about formalistic judging in Central and Ea…
Differentially-private text generation degrades output language quality
Erion Çano, Ivan Habernal
Ensuring user privacy by synthesizing data from large language models (LLMs) tuned under differential privacy (DP) has become popular recently. However, the impact of DP fine-tuned…
Transparent NLP: Using RAG and LLM Alignment for Privacy Q&A
Anna Leschanowsky, Zahra Kolagar, Erion Çano +4
The transparency principle of the General Data Protection Regulation (GDPR) requires data processing information to be clear, precise, and accessible. While language models show pr…
A Comprehensive Survey on Legal Summarization: Challenges and Future Directions
Mousumi Akter, Erion Çano, Erik Weber +2
This article provides a systematic up-to-date survey of automatic summarization techniques, datasets, models, and evaluation methods in the legal domain. Through specific source se…