Automatic evaluation of language models for low-resource Finno-Ugric languages
Organisatsiooni nimi
TartuNLP
Kokkuvõte
In spring 2024, we gathered some human feedback for our new Finno-Ugric language models. Since collecting human feedback is both expensive and time-consuming, it would be good to replace it with feedback from stronger language models, such as OpenAI models. The so-called LLM-judges have been shown to correlate well with human evaluations in English and several other high-resource languages. The goal of this thesis is to investigate whether LLM-judges can also be used to automate the evaluation of language models for low-resource Finno-Ugric languages (specifically Estonian, Võro, Livonian, and Komi).
Lõputöö kaitsmise aasta
2024-2025
Juhendaja
Hele-Andra Kuulmets
Suhtlemiskeel(ed)
eesti keel, inglise keel
Nõuded kandideerijale
Tase
Magister
Märksõnad
Kandideerimise kontakt
Nimi
Hele-Andra Kuulmets
Tel
E-mail