title: SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization
    of Scientific Topics
summary: Prompt-based fine-tuning has become an essential method for eliciting
  information encoded in pre-trained language models for a variety of tasks,
  including text classification. For multi-class classification tasks,
  prompt-based fine-tuning under low-resource scenarios has resulted in
  performance levels comparable to those of fully fine-tuning methods. Previous
  studies have used crafted prompt templates and verbalizers, mapping from the
  label terms space to the class space, to solve the classification problem as a
  masked language modeling task. However, cross-domain and fine-grained
  prompt-based fine-tuning with an automatically enriched verbalizer remains
  unexplored, mainly due to the difficulty and costs of manually selecting domain
  label terms for the verbalizer, which requires humans with domain expertise. To
  address this challenge, we introduce SciPrompt, a framework designed to
  automatically retrieve scientific topic-related terms for low-resource text
  classification tasks. To this end, we select semantically correlated and
  domain-specific label terms within the context of scientific literature for
  verbalizer augmentation. Furthermore, we propose a new verbalization strategy
  that uses correlation scores as additional weights to enhance the prediction
  performance of the language model during model tuning. Our method outperforms
  state-of-the-art, prompt-based fine-tuning methods on scientific text
  classification tasks under few and zero-shot settings, especially in
  classifying fine-grained and emerging scientific topics.
paper_authors._id: 66ff42a93d0c8d4cc39102d0
paper_authors.name: Zhiwen You
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-04T07:31:13.016Z
paper_authors._id: 632d21bb930d186432dfddea
paper_authors.avatarUrl: /avatars/efafd9c17f2f2b69407707c560ea1237.svg
paper_authors.fullname: Zhiwen You
paper_authors.type: user
paper_authors.user: uzw
paper_authors._id: 66ff42a93d0c8d4cc39102d1
paper_authors.name: Kanyao Han
paper_authors._id: 66ff42a93d0c8d4cc39102d2
paper_authors.name: Haotian Zhu
paper_authors._id: 66ff42a93d0c8d4cc39102d3
paper_authors.name: Bertram Ludäscher
paper_authors._id: 66ff42a93d0c8d4cc39102d4
paper_authors.name: Jana Diesner
paper_title: SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization
    of Scientific Topics
paper_summary: Prompt-based fine-tuning has become an essential method for eliciting
  information encoded in pre-trained language models for a variety of tasks,
  including text classification. For multi-class classification tasks,
  prompt-based fine-tuning under low-resource scenarios has resulted in
  performance levels comparable to those of fully fine-tuning methods. Previous
  studies have used crafted prompt templates and verbalizers, mapping from the
  label terms space to the class space, to solve the classification problem as a
  masked language modeling task. However, cross-domain and fine-grained
  prompt-based fine-tuning with an automatically enriched verbalizer remains
  unexplored, mainly due to the difficulty and costs of manually selecting domain
  label terms for the verbalizer, which requires humans with domain expertise. To
  address this challenge, we introduce SciPrompt, a framework designed to
  automatically retrieve scientific topic-related terms for low-resource text
  classification tasks. To this end, we select semantically correlated and
  domain-specific label terms within the context of scientific literature for
  verbalizer augmentation. Furthermore, we propose a new verbalization strategy
  that uses correlation scores as additional weights to enhance the prediction
  performance of the language model during model tuning. Our method outperforms
  state-of-the-art, prompt-based fine-tuning methods on scientific text
  classification tasks under few and zero-shot settings, especially in
  classifying fine-grained and emerging scientific topics.
paper_ai_summary: SciPrompt automates the retrieval of domain-specific label terms to improve prompt-based fine-tuning, achieving better performance than existing methods in scientific text classification, particularly for fine-grained and emerging topics with limited data.
paper_ai_keywords: prompt-based fine-tuning pre-trained language models text classification multi-class classification low-resource scenarios masked language modeling verbalizers cross-domain fine-grained SciPrompt semantically correlated verbalization strategy few-shot settings zero-shot settings