64 citations · 72 across the 13 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…
OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources
Martin Docekal, Martin Fajcik, Pavel Smrz
This paper introduces OARelatedWork: a dataset for related work generation from open-access sources. It is the first large-scale multi-document summarization dataset for related wo…
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model
Martin Fajcik, Muskaan Singh, Juan Zuluaga-Gomez +4
In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus. The challenge focused on the automatic de…
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Sergio Burdisso, Juan Zuluaga-Gomez, Esau Villatoro-Tello +4
In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (C…
Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction
Martin Fajcik, Petr Motlicek, Pavel Smrz
We present Claim-Dissector: a novel latent variable model for fact-checking and analysis, which given a claim and a set of retrieved evidences jointly learns to identify: (i) the r…
R2-D2: A Modular Baseline for Open-Domain Question Answering
Martin Fajcik, Martin Docekal, Karel Ondrej +1
This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generati…
Pruning the Index Contents for Memory Efficient Open-Domain QA
Martin Fajcik, Martin Docekal, Karel Ondrej +1
This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches. Specifically, it proposes the novel R2-D2 (Rank twic…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
JokeMeter at SemEval-2020 Task 7: Convolutional humor
Martin Docekal, Martin Fajcik, Josef Jon +1
This paper describes our system that was designed for Humor evaluation within the SemEval-2020 Task 7. The system is based on convolutional neural network architecture. We investig…
BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense
Josef Jon, Martin Fajčík, Martin Dočekal +1
This paper describes work of the BUT-FIT's team at SemEval 2020 Task 4 - Commonsense Validation and Explanation. We participated in all three subtasks. In subtasks A and B, our sub…
Rethinking the Objectives of Extractive Question Answering
Martin Fajcik, Josef Jon, Pavel Smrz
This work demonstrates that using the objective with independence assumption for modelling the span probability of span starting at position and e…
BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models
Martin Fajcik, Josef Jon, Martin Docekal +1
This paper describes BUT-FIT's submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a gi…
BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers
Martin Fajcik, Lukáš Burget, Pavel Smrz
This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challe…
Automation of Processor Verification Using Recurrent Neural Networks
Martin Fajcik, Marcela Zachariasova, Pavel Smrz
When considering simulation-based verification of processors, the current trend is to generate stimuli using pseudorandom generators (PRGs), apply them to the processor inputs and…