title: LLM Teacher-Student Framework for Text Classification With No Manually
    Annotated Data: A Case Study in IPTC News Topic Classification
summary: With the ever-increasing number of news stories available online, classifying
  them by topic, regardless of the language they are written in, has become
  crucial for enhancing readers' access to relevant content. To address this
  challenge, we propose a teacher-student framework based on large language
  models (LLMs) for developing multilingual news classification models of
  reasonable size with no need for manual data annotation. The framework employs
  a Generative Pretrained Transformer (GPT) model as the teacher model to develop
  an IPTC Media Topic training dataset through automatic annotation of news
  articles in Slovenian, Croatian, Greek, and Catalan. The teacher model exhibits
  a high zero-shot performance on all four languages. Its agreement with human
  annotators is comparable to that between the human annotators themselves. To
  mitigate the computational limitations associated with the requirement of
  processing millions of texts daily, smaller BERT-like student models are
  fine-tuned on the GPT-annotated dataset. These student models achieve high
  performance comparable to the teacher model. Furthermore, we explore the impact
  of the training data size on the performance of the student models and
  investigate their monolingual, multilingual and zero-shot cross-lingual
  capabilities. The findings indicate that student models can achieve high
  performance with a relatively small number of training instances, and
  demonstrate strong zero-shot cross-lingual abilities. Finally, we publish the
  best-performing news topic classifier, enabling multilingual classification
  with the top-level categories of the IPTC Media Topic schema.
paper_authors._id: 674d5e4ee208467411046583
paper_authors.name: Taja Kuzman
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-12-02T07:21:13.856Z
paper_authors._id: 636e15698ba65db4a09384ab
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/636e15698ba65db4a09384ab/jtkbytatqb7oiIIB3oWMF.jpeg
paper_authors.fullname: Taja Kuzman
paper_authors.type: user
paper_authors.user: TajaKuzman
paper_authors._id: 674d5e4ee208467411046584
paper_authors.name: Nikola Ljubešić
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-02T09:46:03.442Z
paper_authors._id: 601431aad1f82f3c09cb43ac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1632563266956-601431aad1f82f3c09cb43ac.png
paper_authors.fullname: Nikola Ljubešić
paper_authors.type: user
paper_authors.user: nljubesi
paper_title: LLM Teacher-Student Framework for Text Classification With No Manually
    Annotated Data: A Case Study in IPTC News Topic Classification
paper_summary: With the ever-increasing number of news stories available online, classifying
  them by topic, regardless of the language they are written in, has become
  crucial for enhancing readers' access to relevant content. To address this
  challenge, we propose a teacher-student framework based on large language
  models (LLMs) for developing multilingual news classification models of
  reasonable size with no need for manual data annotation. The framework employs
  a Generative Pretrained Transformer (GPT) model as the teacher model to develop
  an IPTC Media Topic training dataset through automatic annotation of news
  articles in Slovenian, Croatian, Greek, and Catalan. The teacher model exhibits
  a high zero-shot performance on all four languages. Its agreement with human
  annotators is comparable to that between the human annotators themselves. To
  mitigate the computational limitations associated with the requirement of
  processing millions of texts daily, smaller BERT-like student models are
  fine-tuned on the GPT-annotated dataset. These student models achieve high
  performance comparable to the teacher model. Furthermore, we explore the impact
  of the training data size on the performance of the student models and
  investigate their monolingual, multilingual and zero-shot cross-lingual
  capabilities. The findings indicate that student models can achieve high
  performance with a relatively small number of training instances, and
  demonstrate strong zero-shot cross-lingual abilities. Finally, we publish the
  best-performing news topic classifier, enabling multilingual classification
  with the top-level categories of the IPTC Media Topic schema.
paper_ai_keywords: teacher-student framework large language models (LLMs) Generative Pretrained Transformer (GPT) IPTC Media Topic training dataset zero-shot performance BERT-like student models fine-tuned multilingual classification zero-shot cross-lingual abilities top-level categories of the IPTC Media Topic schema