% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
%Task description paper


@article{banerjee2019comparative,
  title={Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification},
  author={Banerjee, Imon and Ling, Yuan and Chen, Matthew C and Hasan, Sadid A and Langlotz, Curtis P and Moradzadeh, Nathaniel and Chapman, Brian and Amrhein, Timothy and Mong, David and Rubin, Daniel L and others},
  journal={Artificial intelligence in medicine},
  volume={97},
  pages={79--88},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{naseem-etal-2026-polar,
title = {{S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online
Polarization},
author = {Naseem, Usman and
 Geislinger, Robert and
 Ren, Juan and
 Kohail, Sarah and
 Garrido Veliz, Rudy and
 Sam Sahil, P and
 Zhang, Yiran and
 Stranisci, Marco Antonio and
 Abdulmumin, Idris and
 Alacam, {"O}zge and
 Acar{"u}rk, Cengiz and
 Jabr, Aisha and
 Anwar, Saba and
 Ayele, Abinew Ali and
 Tutubalina, Elena and
 Htet, Aung Kyaw and
 Wang, Xintong and
 Thapa, Surendrabikram and
 Chakraborty, Tanmoy and
 Kodati, Dheeraj and
 Moradizeyveh, Sahar and
 Alam, Firoj and
 Thu, Ye Kyaw and
 Parida, Shantipriya and
 Qazi, Ihsan Ayyub and
 Onyango, Nelson Odhiambo and
 Siro, Clemencia and
 Ahmad, Ibrahim Said and
 Wanzare, Lilian and
 Ali, Adem Chanie and
 Semmann, Martin and
 Biemann, Chris and
 Muhammad, Shamsuddeen Hassan and
 Yimam, Seid Muhie},
booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval2026)},
year = {2026},
publisher = {Association for Computational Linguistics},
}
@article{Sebastiani_2002,
   title={Machine learning in automated text categorization},
   volume={34},
   ISSN={1557-7341},
   url={http://dx.doi.org/10.1145/505282.505283},
   DOI={10.1145/505282.505283},
   number={1},
   journal={ACM Computing Surveys},
   publisher={Association for Computing Machinery (ACM)},
   author={Sebastiani, Fabrizio},
   year={2002},
   month=mar, pages={1–47} }



@misc{naseem2026polarbenchmarkmultilingualmulticultural,
 title={POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event
Online Polarization},
 author={Usman Naseem and Robert Geislinger and Juan Ren and Sarah Kohail and
Rudy Garrido Veliz and P Sam Sahil and Yiran Zhang and Marco Antonio Stranisci
and Idris Abdulmumin and Özge Alacam and Cengiz Acartürk and Aisha Jabr and
Saba Anwar and Abinew Ali Ayele and Simona Frenda and Alessandra Teresa
Cignarella and Elena Tutubalina and Oleg Rogov and Aung Kyaw Htet and Xintong
Wang and Surendrabikram Thapa and Kritesh Rauniyar and Tanmoy Chakraborty and
Arfeen Zeeshan and Dheeraj Kodati and Satya Keerthi and Sahar Moradizeyveh and
Firoj Alam and Arid Hasan and Syed Ishtiaque Ahmed and Ye Kyaw Thu and
Shantipriya Parida and Ihsan Ayyub Qazi and Lilian Wanzare and Nelson Odhiambo
Onyango and Clemencia Siro and Jane Wanjiru Kimani and Ibrahim Said Ahmad and
Adem Chanie Ali and Martin Semmann and Chris Biemann and Shamsuddeen Hassan
Muhammad and Seid Muhie Yimam},
 year={2026},
 eprint={2505.20624},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 url={https://arxiv.org/abs/2505.20624}, 
}
@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423/",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
}

@misc{yousefiramandi2025finetuningcausalllmstext,
      title={Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches}, 
      author={Amirhossein Yousefiramandi and Ciaran Cooney},
      year={2025},
      eprint={2512.12677},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.12677}, 
}
@article{10.1145/3706119,
author = {Fatemi, Sorouralsadat and Hu, Yuheng and Mousavi, Maryam},
title = {A Comparative Analysis of Instruction Fine-Tuning Large Language Models for Financial Text Classification},
year = {2025},
issue_date = {March 2025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {16},
number = {1},
issn = {2158-656X},
url = {https://doi.org/10.1145/3706119},
doi = {10.1145/3706119},
abstract = {Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks, including language understanding, reasoning, and generation. However, general-domain LLMs often struggle with financial tasks due to the technical and specialized nature of financial texts. This study investigates the efficacy of instruction fine-tuning smaller-scale LLMs, including Mistral-7B, Llama3-8B, and Phi3-mini, to enhance their performance in financial text classification tasks. We fine-tuned both instruction-tuned and base models across four financial classification tasks, achieving significant improvements in task-specific performance. Furthermore, we evaluated the zero-shot capabilities of these fine-tuned models on three unseen complex financial tasks, including argument classification, deal completeness classification, and causal classification. Our results indicate while base model fine-tuning led to greater degradation, instruction-tuned models maintained more robust performance. To address this degradation, we employed model merging techniques, integrating single-task domain-specific fine-tuned models with the base model. Using this merging method resulted in significant enhancements in zero-shot performance, even exceeding the original model’s accuracy on certain datasets. Our findings underscore the effectiveness of instruction fine-tuning and model merging for adapting LLMs to specialized financial text classification tasks.},
journal = {ACM Trans. Manage. Inf. Syst.},
month = feb,
articleno = {6},
numpages = {30},
keywords = {Large language models, parameter-efficient fine-tuning, instruction fine-tuning, text classification, finance}
}



@article{pedregosa2018scikitlearnmachinelearningpython,
author = {Pedregosa, Fabian and Varoquaux, Ga\"{e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, \'{E}douard},
title = {Scikit-learn: Machine Learning in Python},
year = {2011},
issue_date = {2/1/2011},
publisher = {JMLR.org},
volume = {12},
number = {null},
issn = {1532-4435},
abstract = {Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.},
journal = {J. Mach. Learn. Res.},
month = nov,
pages = {2825–2830},
numpages = {6}
}


@inproceedings{wolf2020huggingfacestransformersstateoftheartnatural,
    title = "Transformers: State-of-the-Art Natural Language Processing",
    author = "Wolf, Thomas  and
      Debut, Lysandre  and
      Sanh, Victor  and
      Chaumond, Julien  and
      Delangue, Clement  and
      Moi, Anthony  and
      Cistac, Pierric  and
      Rault, Tim  and
      Louf, Remi  and
      Funtowicz, Morgan  and
      Davison, Joe  and
      Shleifer, Sam  and
      von Platen, Patrick  and
      Ma, Clara  and
      Jernite, Yacine  and
      Plu, Julien  and
      Xu, Canwen  and
      Le Scao, Teven  and
      Gugger, Sylvain  and
      Drame, Mariama  and
      Lhoest, Quentin  and
      Rush, Alexander",
    editor = "Liu, Qun  and
      Schlangen, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-demos.6/",
    doi = "10.18653/v1/2020.emnlp-demos.6",
    pages = "38--45",
    abstract = "Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at \url{https://github.com/huggingface/transformers}."
}




@misc{ashiga2025ensemblelearninglargelanguage,
      title={Ensemble Learning for Large Language Models in Text and Code Generation: A Survey}, 
      author={Mari Ashiga and Wei Jie and Fan Wu and Vardan Voskanyan and Fateme Dinmohammadi and Paul Brookes and Jingzhi Gong and Zheng Wang},
      year={2025},
      eprint={2503.13505},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.13505}, 
}
@article{DBLP:journals/corr/abs-2503-13505,
  author       = {Mari Ashiga and
                  Wei Jie and
                  Fan Wu and
                  Vardan Voskanyan and
                  Fateme Dinmohammadi and
                  Paul Brookes and
                  Jingzhi Gong and
                  Zheng Wang},
  title        = {Ensemble Learning for Large Language Models in Text and Code Generation:
                  {A} Survey},
  journal      = {CoRR},
  volume       = {abs/2503.13505},
  year         = {2025},
  url          = {https://doi.org/10.48550/arXiv.2503.13505},
  doi          = {10.48550/ARXIV.2503.13505},
  eprinttype    = {arXiv},
  eprint       = {2503.13505},
  timestamp    = {Mon, 30 Jun 2025 07:52:51 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2503-13505.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{10.1145/3747588,
author = {Jiang, Juyong and Wang, Fan and Shen, Jiasi and Kim, Sungju and Kim, Sunghun},
title = {A Survey on Large Language Models for Code Generation},
year = {2026},
issue_date = {February 2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {35},
number = {2},
issn = {1049-331X},
url = {https://doi.org/10.1145/3747588},
doi = {10.1145/3747588},
abstract = {Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks, known as Code LLMs, particularly in code generation that generates source code with LLM from natural language descriptions. This burgeoning field has captured significant interest from both academic researchers and industry professionals due to its practical significance in software development, e.g., GitHub Copilot. Despite the active exploration of LLMs for a variety of code tasks, either from the perspective of Natural Language Processing (NLP) or Software Engineering (SE) or both, there is a noticeable absence of a comprehensive and up-to-date literature review dedicated to LLM for code generation. In this survey, we aim to bridge this gap by providing a systematic literature review that serves as a valuable reference for researchers investigating the cutting-edge progress in LLMs for code generation. We introduce a taxonomy to categorize and discuss the recent developments in LLMs for code generation, covering aspects such as data curation, latest advances, performance evaluation, ethical implications, environmental impact, and real-world applications. In addition, we present a historical overview of the evolution of LLMs for code generation and provide a quantitative and qualitative comparative analysis of experimental results of code LLMs, sourced from their original papers to ensure a fair comparison on the HumanEval, MBPP, and BigCodeBench benchmarks, across various levels of difficulty and types of programming tasks, to highlight the progressive enhancements in LLM capabilities for code generation. We identify critical challenges and promising opportunities regarding the gap between academia and practical development. Furthermore, we have established a dedicated resource GitHub page () to continuously document and disseminate the most recent advances in the field.},
journal = {ACM Trans. Softw. Eng. Methodol.},
month = jan,
articleno = {58},
numpages = {72},
keywords = {Large Language Models, Code Large Language Models, Code Generation}
}



@article{minaee2021deeplearningbasedtext,
author = {Minaee, Shervin and Kalchbrenner, Nal and Cambria, Erik and Nikzad, Narjes and Chenaghlu, Meysam and Gao, Jianfeng},
title = {Deep Learning--based Text Classification: A Comprehensive Review},
year = {2021},
issue_date = {April 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {54},
number = {3},
issn = {0360-0300},
url = {https://doi.org/10.1145/3439726},
doi = {10.1145/3439726},
abstract = {Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.},
journal = {ACM Comput. Surv.},
month = apr,
articleno = {62},
numpages = {40},
keywords = {topic classification, sentiment analysis, question answering, news categorization, natural language inference, deep learning, Text classification}
}




@inproceedings{devlin2019bertpretrainingdeepbidirectional,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423/",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
}

@article{radford2019language,
  title={Language Models are Unsupervised Multitask Learners},
  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  year={2019}
}