Quelltext der Seite Exploring The Robustness Of The Natural Language Inference Capabilties Of T5

{{Vortrag
|vortragender=Dennis Grötzinger
|email=dennis.groetzinger98@gmail.com
|vortragstyp=Bachelorarbeit
|betreuer=Jan Keim
|termin=Institutsseminar/2021-07-09
|kurzfassung=Large language models like T5 perform excellently on various NLI benchmarks. However, it has been shown that even small changes in the structure of these tasks can significantly reduce accuracy. I build upon this insight and explore how robust the NLI skills of T5 are in three scenarios. First, I show that T5 is robust to some variations in the MNLI pattern, while others degenerate performance significantly. Second, I observe that some other patterns that T5 was trained on can be substituted for the MNLI pattern and still achieve good results. Third, I demonstrate that the MNLI pattern translate well to other NLI datasets, even improving accuracy by 13% in the case of RTE. All things considered, I conclude that the robustness of the NLI skills of T5 really depend on which alterations are applied.
}}