metadata

license: apache-2.0
datasets:
  - kejian/ACL-ARC
language:
  - en
metrics:
  - f1
base_model:
  - Qwen/Qwen2.5-14B-Instruct
library_name: transformers
tags:
  - scientometrics
  - citation_analysis
  - citation_intent_classification
pipeline_tag: zero-shot-classification

Qwen2.5-14-CIC-ACLARC

A fine-tuned model for Citation Intent Classification, based on Qwen 2.5 14B Instruct and trained on the ACL-ARC dataset.

GGUF Version

ACL-ARC classes

Class	Description
Background	The cited paper provides relevant Background information or is part of the body of literature.
Motivation	The citing paper is directly motivated by the cited paper.
Uses	The citing paper uses the methodology or tools created by the cited paper.
Extension	The citing paper extends the methods, tools or data, etc. of the cited paper.
Comparison or Contrast	The citing paper expresses similarities or differences to, or disagrees with, the cited paper.
Future	*The cited paper may be a potential avenue for future work.

Quickstart

TODO

Details about the system prompts and query templates can be found in the paper.

There might be a need for a cleanup function to extract the predicted label from the output. You can find ours on GitHub.

Citation

@misc{koloveas2025llmspredictcitationintent,
      title={Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs}, 
      author={Paris Koloveas and Serafeim Chatzopoulos and Thanasis Vergoulis and Christos Tryfonopoulos},
      year={2025},
      eprint={2502.14561},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.14561}, 
}