64 citations · 72 across the 12 of their papers we have counts for
19 papers
Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors
Francisco Valentini, Edgar Altszyler, Martin Fajcik
Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs. Yet annotation protocols may not purely reflect relevance: they select only…
Examining the Metrics for Document-Level Claim Extraction in Czech and Slovak
Lucia Makaiova, Martin Fajcik, Antonin Jarolim
Document-level claim extraction remains an open challenge in the field of fact-checking, and subsequently, methods for evaluating extracted claims have received limited attention.…
Can LLMs extract human-like fine-grained evidence for evidence-based fact-checking?
Antonín Jarolím, Martin Fajčík, Lucia Makaiová
Misinformation frequently spreads in user comments under online news articles, highlighting the need for effective methods to detect factually incorrect information. To strongly su…
On Recipe Memorization and Creativity in Large Language Models: Is Your Model a Creative Cook, a Bad Cook, or Merely a Plagiator?
Jan Kvapil, Martin Fajcik
This work-in-progress investigates the memorization, creativity, and nonsense found in cooking recipes generated from Large Language Models (LLMs). Precisely, we aim (i) to analyze…
A Comparative Study of Text Retrieval Models on DaReCzech
Jakub Stetina, Martin Fajcik, Michal Stefanik +1
This article presents a comprehensive evaluation of 7 off-the-shelf document retrieval models: Splade, Plaid, Plaid-X, SimCSE, Contriever, OpenAI ADA and Gemma2 chosen to determine…
BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanism
Martin Fajcik, Martin Docekal, Jan Dolezal +15
We present BenCzechMark (BCM), the first comprehensive Czech language benchmark designed for large language models, offering diverse tasks, multiple task formats, and multiple eval…