1 citations · 1 across the 6 of their papers we have counts for
6 papers
KARLA: Knowledge-base Augmented Retrieval for Language Models
Francois Crespin, Fabian M. Suchanek, Nils Holzenberger
We propose a new method that allows an LLM to automatically pull in factual knowledge from a knowledge base during token generation. This means that (1)~factual knowledge in the LL…
LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation
Samy Haffoudhi, Nikola Dobričić, Fabian Suchanek +1
Entity linking is a key component of many downstream NLP systems, yet existing approaches are often tied to the specific target knowledge bases and domains, limiting their real wor…
Where Experts Disagree, Models Fail: Detecting Implicit Legal Citations in French Court Decisions
Avrile Floro, Tamara Dhorasoo, Soline Pellez +1
Applying computational methods to law at scale requires separating genuine legal reasoning from surface similarity. We study this through a concrete task: detecting implicit citati…
LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation
Samy Haffoudhi, Fabian M. Suchanek, Nils Holzenberger
Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construction, question-answering, and in…
Retrieval-Constrained Decoding Reveals Underestimated Parametric Knowledge in Language Models
Rajaa El Hamdani, Samy Haffoudhi, Nils Holzenberger +3
Language models (LMs) encode substantial factual knowledge, but often produce answers judged as incorrect. We hypothesize that many of these answers are actually correct, but are e…
The Factuality of Large Language Models in the Legal Domain
Rajaa El Hamdani, Thomas Bonald, Fragkiskos Malliaros +2
This paper investigates the factuality of large language models (LLMs) as knowledge bases in the legal domain, in a realistic usage scenario: we allow for acceptable variations in…