Survey of Large Language Models on the Text Generation Task

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F24%3A43925828" target="_blank" >RIV/62156489:43110/24:43925828 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.11118/978-80-7509-990-7-0195" target="_blank" >https://doi.org/10.11118/978-80-7509-990-7-0195</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/978-80-7509-990-7-0195" target="_blank" >10.11118/978-80-7509-990-7-0195</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Survey of Large Language Models on the Text Generation Task

Popis výsledku v původním jazyce

This paper focuses on the comparison of GPT, GPT-2, XLNet, T5 models on text generation tasks. None of the autoencoder models are included in the comparison ranking due to their unsuitability for text generation tasks. The comparison of the models was performed using the BERT-score metric, which calculates precision, recall and F1 values for each sentence. The median was used to obtain the final results from this metric. A preprocessed dataset of empathetic dialogues was used to test the models, which is presented in this paper and compared with other datasets containing dialogues in English. The tested models were only pre-trained and there was no fine-tune on the dataset used for testing. The transformers library from Hugging face and the Python language were used to test the models. The research showed on the pre-trained dataset empathic dialogues has the highest precision model T5, recall and F1 has the highest precision model GPT-2.

Název v anglickém jazyce

Survey of Large Language Models on the Text Generation Task

Popis výsledku anglicky

This paper focuses on the comparison of GPT, GPT-2, XLNet, T5 models on text generation tasks. None of the autoencoder models are included in the comparison ranking due to their unsuitability for text generation tasks. The comparison of the models was performed using the BERT-score metric, which calculates precision, recall and F1 values for each sentence. The median was used to obtain the final results from this metric. A preprocessed dataset of empathetic dialogues was used to test the models, which is presented in this paper and compared with other datasets containing dialogues in English. The tested models were only pre-trained and there was no fine-tune on the dataset used for testing. The transformers library from Hugging face and the Python language were used to test the models. The research showed on the pre-trained dataset empathic dialogues has the highest precision model T5, recall and F1 has the highest precision model GPT-2.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_017%2F0002334" target="_blank" >EF16_017/0002334: Výzkumná infrastruktura pro mladé vědce</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

26th International Conference Economic Competitiveness and Sustainability: Proceedings

ISBN

978-80-7509-990-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

195-200

Název nakladatele

Mendelova univerzita v Brně

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

21. 3. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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