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}, 
}
Downloads last month
9
Safetensors
Model size
14.8B params
Tensor type
BF16
·
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.

Model tree for sknow-lab/Qwen2.5-14B-CIC-ACLARC

Base model

Qwen/Qwen2.5-14B
Finetuned
(89)
this model
Quantizations
2 models

Dataset used to train sknow-lab/Qwen2.5-14B-CIC-ACLARC

Collection including sknow-lab/Qwen2.5-14B-CIC-ACLARC