#llms4subjects — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #llms4subjects, aggregated by home.social.
-
Earlier this year, the Annif team participated in the LLMs4Subjects challenge, where our automated indexing tool performed nicely! 🏆 We also got new ideas for Annif development out of the challenge! The SemEval-2025 workshop proceedings are now available 👉 https://aclanthology.org/volumes/2025.semeval-1/
The work continued with the GermEval workshop, focusing on resource efficiency, and we did very well!🥇Check out our GermEval pre-print 👉 https://doi.org/10.48550/arXiv.2508.15877 🤖
#LLMs4Subjects #Annif #SemEval2025 #AI #SubjectIndexing -
Annif-tiimi osallistui alkuvuodesta LLMs4Subjects-haasteeseen, jossa automaattisen asiasanoituksen työkalumme pärjäsi hienosti! 🏆 Saimme kisasta uusia ideoita Annifin kehitykseen! SemEval-2025-työpajan julkaisut ovat nyt luettavissa 👉 https://aclanthology.org/volumes/2025.semeval-1/
Haaste jatkui GermEval-työpajan muodossa, jossa keskityttiin mallien resurssitehokkuuteen, ja sijoituimme taas mainiosti!🥇Lue esijulkaisumme GermEval-kisasta 👉 https://doi.org/10.48550/arXiv.2508.15877 🤖
#LLMs4Subjects #Annif #SemEval2025 #AI #Asiasanoitus -
We are also participating in the 2nd #LLMs4Subjects challenge at the GermEval-2025 workshop. Expect to see an even better automated subject indexing system based on combining #Annif and #LLMs!
https://sites.google.com/view/llms4subjects-germeval/home
The theme for the 2nd installment is "energy- and compute-efficient LLMs", so we will focus on building solutions based on smaller, more efficient, local AI models rather than huge, general purpose cloud LLMs.
-
In the first #LLMs4Subjects challenge at the SemEval-2025 workshop, our #Annif team did very well!
The challenge was to generate good quality subject indexing for bibliographic records in German & English using LLMs. We used LLMs for data preprocessing (translation & synthetic data) and Annif as the main suggestion engine. We ranked 1st and 2nd in quantitative and 4th in qualitative evaluations out of 14 teams!
More info & preprints: https://groups.google.com/g/annif-users/c/b8kVy6XSzB4/m/JE6xBzSuEgAJ
-
In the first #LLMs4Subjects challenge at the SemEval-2025 workshop, our #Annif team did very well!
The challenge was to generate good quality subject indexing for bibliographic records in German & English using LLMs. We used LLMs for data preprocessing (translation & synthetic data) and Annif as the main suggestion engine. We ranked 1st and 2nd in quantitative and 4th in qualitative evaluations out of 14 teams!
More info & preprints: https://groups.google.com/g/annif-users/c/b8kVy6XSzB4/m/JE6xBzSuEgAJ
-
In the first #LLMs4Subjects challenge at the SemEval-2025 workshop, our #Annif team did very well!
The challenge was to generate good quality subject indexing for bibliographic records in German & English using LLMs. We used LLMs for data preprocessing (translation & synthetic data) and Annif as the main suggestion engine. We ranked 1st and 2nd in quantitative and 4th in qualitative evaluations out of 14 teams!
More info & preprints: https://groups.google.com/g/annif-users/c/b8kVy6XSzB4/m/JE6xBzSuEgAJ
-
In the first #LLMs4Subjects challenge at the SemEval-2025 workshop, our #Annif team did very well!
The challenge was to generate good quality subject indexing for bibliographic records in German & English using LLMs. We used LLMs for data preprocessing (translation & synthetic data) and Annif as the main suggestion engine. We ranked 1st and 2nd in quantitative and 4th in qualitative evaluations out of 14 teams!
More info & preprints: https://groups.google.com/g/annif-users/c/b8kVy6XSzB4/m/JE6xBzSuEgAJ
-
In the first #LLMs4Subjects challenge at the SemEval-2025 workshop, our #Annif team did very well!
The challenge was to generate good quality subject indexing for bibliographic records in German & English using LLMs. We used LLMs for data preprocessing (translation & synthetic data) and Annif as the main suggestion engine. We ranked 1st and 2nd in quantitative and 4th in qualitative evaluations out of 14 teams!
More info & preprints: https://groups.google.com/g/annif-users/c/b8kVy6XSzB4/m/JE6xBzSuEgAJ