19 citations · 34 across the 22 of their papers we have counts for
5 papers · 1 filter
SRS-Stories: Vocabulary-constrained multilingual story generation for language learning
Wiktor Kamzela, Mateusz Lango, Ondrej Dusek
In this paper, we use large language models to generate personalized stories for language learners, using only the vocabulary they know. The generated texts are specifically writte…
LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators
Mateusz Lango, Ondřej Dušek
We present a novel neurosymbolic framework for RDF-to-text generation, in which the model is "trained" through collaborative interactions among multiple LLM agents rather than trad…
OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs
Ivan Kartáč, Mateusz Lango, Ondřej Dušek
Large Language Models (LLMs) have demonstrated great potential as evaluators of NLG systems, allowing for high-quality, reference-free, and multi-aspect assessments. However, exist…
Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems
Jędrzej Warczyński, Mateusz Lango, Ondrej Dusek
We introduce a simple approach that uses a large language model (LLM) to automatically implement a fully interpretable rule-based data-to-text system in pure Python. Experimental e…
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish
Marta Lango, Borys Naglik, Mateusz Lango +1
Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. It concerns the construction of triplets that contain an aspect,…