SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization
  of Scientific Topics
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.
66ff42a93d0c8d4cc39102d0
Zhiwen You
claimed_verified
2024-10-04T07:31:13.016Z
632d21bb930d186432dfddea
/avatars/efafd9c17f2f2b69407707c560ea1237.svg
Zhiwen You
user
uzw
66ff42a93d0c8d4cc39102d1
Kanyao Han
66ff42a93d0c8d4cc39102d2
Haotian Zhu
66ff42a93d0c8d4cc39102d3
Bertram Ludäscher
66ff42a93d0c8d4cc39102d4
Jana Diesner
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization
  of Scientific Topics
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.
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.
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