Institutsseminar/2021-07-09
| Datum | Freitag, 9. Juli 2021 | |
|---|---|---|
| Uhrzeit | 14:00 – 14:30 Uhr (Dauer: 30 min) | |
| Ort | ||
| Prüfer/in | ||
| Webkonferenz | https://sdqweb.ipd.kit.edu/wiki/Institutsseminar/Microsoft Teams | |
| Vorheriger Termin | Fr 2. Juli 2021 | |
| Nächster Termin | Fr 16. Juli 2021 |
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Vorträge
| Vortragende(r) | Dennis Grötzinger |
|---|---|
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Jan Keim |
| Vortragssprache | |
| Vortragsmodus | |
| 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. |
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