36 citations · 41 across the 4 of their papers we have counts for
4 papers
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Leveraging pre-trained language models for conversational information seeking from text
Patrizio Bellan, Mauro Dragoni, Chiara Ghidini
Recent advances in Natural Language Processing, and in particular on the construction of very large pre-trained language representation models, is opening up new perspectives on th…
PET: An Annotated Dataset for Process Extraction from Natural Language Text
Patrizio Bellan, Han van der Aa, Mauro Dragoni +2
Process extraction from text is an important task of process discovery, for which various approaches have been developed in recent years. However, in contrast to other information…
Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges
Patrizio Bellan, Mauro Dragoni, Chiara Ghidini +2
The extraction of process models from text refers to the problem of turning the information contained in an unstructured textual process descriptions into a formal representation,i…