We (
text
We (
@osma
mention
osma
, 
text
, 
@MonaLehtinen
mention
MonaLehtinen
 & me, i.e. the Annif team at the National Library of Finland) recently took part in the LLMs4Subjects challenge at the SemEval-2025 workshop. The task was to use large language models (LLMs) to generate good quality subject indexing for bibliographic records, i.e. titles and abstracts.
text
 & me, i.e. the Annif team at the National Library of Finland) recently took part in the LLMs4Subjects challenge at the SemEval-2025 workshop. The task was to use large language models (LLMs) to generate good quality subject indexing for bibliographic records, i.e. titles and abstracts.
new_line
new_line
We are glad to report that our system performed well; it was ranked
text
We are glad to report that our system performed well; it was ranked
new_line
new_line
🥇 1st in the category where the full vocabulary was used
text
🥇 1st in the category where the full vocabulary was used
new_line
🥈 2nd in the smaller vocabulary category
text
🥈 2nd in the smaller vocabulary category
new_line
🏅 4th in the qualitative evaluations.
text
🏅 4th in the qualitative evaluations.
new_line
new_line
14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available.
text
14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available.
new_line
new_line
We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way.
text
We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way.
new_line
new_line
More information can be found in our system description preprint: 
text
More information can be found in our system description preprint: 
Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs (2504.19675)
https://huggingface.co/papers/2504.19675
2504.19675
paper
resource
https://huggingface.co/papers/2504.19675
new_line
new_line
See also the task description preprint: 
text
See also the task description preprint: 
SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject
  Tagging for a National Technical Library's Open-Access Catalog (2504.07199)
https://huggingface.co/papers/2504.07199
2504.07199
paper
resource
https://huggingface.co/papers/2504.07199
new_line
new_line
The Annif models trained for this task are available here: 
text
The Annif models trained for this task are available here: 
https://huggingface.co/NatLibFi/Annif-LLMs4Subjects-data
NatLibFi/Annif-LLMs4Subjects-data
model
resource
https://huggingface.co/NatLibFi/Annif-LLMs4Subjects-data
We (@osma, @MonaLehtinen & me, i.e. the Annif team at the National Library of Finland) recently took part in the LLMs4Subjects challenge at the SemEval-2025 workshop. The task was to use large language models (LLMs) to generate good quality subject indexing for bibliographic records, i.e. titles and abstracts.

We are glad to report that our system performed well; it was ranked

🥇 1st in the category where the full vocabulary was used
🥈 2nd in the smaller vocabulary category
🏅 4th in the qualitative evaluations.

14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available.

We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way.

More information can be found in our system description preprint: https://huggingface.co/papers/2504.19675

See also the task description preprint: https://huggingface.co/papers/2504.07199

The Annif models trained for this task are available here: https://huggingface.co/NatLibFi/Annif-LLMs4Subjects-data
🚀
andreaschandra John6666 xkkm
🤝
eaddario
5e762d233d77a72421292d02
https://cdn-avatars.huggingface.co/v1/production/uploads/1657201383691-5e762d233d77a72421292d02.jpeg
Andreas Chandra
andreaschandra
user
6818cdc2214df3575105f1a7
https://cdn-avatars.huggingface.co/v1/production/uploads/6818cdc2214df3575105f1a7/qz1LS3XAFgnicwgjeCJYG.jpeg
CJ Hauser
CJHauser
user