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

@inproceedings{thomas2024ineversaidthat,
    title = "``{I} Never Said That'': A dataset, taxonomy and baselines on response clarity classification",
    author = "Thomas, Konstantinos  and
      Filandrianos, Giorgos  and
      Lymperaiou, Maria  and
      Zerva, Chrysoula  and
      Stamou, Giorgos",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.300/",
    doi = "10.18653/v1/2024.findings-emnlp.300",
    pages = "5204--5233",
    abstract = "Equivocation and ambiguity in public speech are well-studied discourse phenomena, especially in political science and analysis of political interviews. Inspired by the well-grounded theory on equivocation, we aim to resolve the closely related problem of response clarity in questions extracted from political interviews, leveraging the capabilities of Large Language Models (LLMs) and human expertise. To this end, we introduce a novel taxonomy that frames the task of detecting and classifying response clarity and a corresponding clarity classification dataset which consists of question-answer (QA) pairs drawn from political interviews and annotated accordingly. Our proposed two-level taxonomy addresses the clarity of a response in terms of the information provided for a given question (high-level) and also provides a fine-grained taxonomy of evasion techniques that relate to unclear, ambiguous responses (lower-level). We combine ChatGPT and human annotators to collect, validate and annotate discrete QA pairs from political interviews, to be used for our newly introduced response clarity task. We provide a detailed analysis and conduct several experiments with different model architectures, sizes and adaptation methods to gain insights and establish new baselines over the proposed dataset and task."
}

@inproceedings{wei2022chain,
  author       = {Jason Wei and
                  Xuezhi Wang and
                  Dale Schuurmans and
                  Maarten Bosma and
                  Brian Ichter and
                  Fei Xia and
                  Ed H. Chi and
                  Quoc V. Le and
                  Denny Zhou},
  editor       = {Sanmi Koyejo and
                  S. Mohamed and
                  A. Agarwal and
                  Danielle Belgrave and
                  K. Cho and
                  A. Oh},
  title        = {Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
  booktitle    = {Advances in Neural Information Processing Systems 35: Annual Conference
                  on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans,
                  LA, USA, November 28 - December 9, 2022},
  year         = {2022},
  url          = {http://papers.nips.cc/paper\_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html},
  timestamp    = {Tue, 12 Nov 2024 16:50:49 +0100},
  biburl       = {https://dblp.org/rec/conf/nips/Wei0SBIXCLZ22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{deepseekv3,
      title={DeepSeek-V3 Technical Report}, 
      author={DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J. L. Cai and Jian Liang and Jianzhong Guo and Jiaqi Ni and Jiashi Li and Jiawei Wang and Jin Chen and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and Junxiao Song and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Lei Xu and Leyi Xia and Liang Zhao and Litong Wang and Liyue Zhang and Meng Li and Miaojun Wang and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Mingming Li and Ning Tian and Panpan Huang and Peiyi Wang and Peng Zhang and Qiancheng Wang and Qihao Zhu and Qinyu Chen and Qiushi Du and R. J. Chen and R. L. Jin and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and Runxin Xu and Ruoyu Zhang and Ruyi Chen and S. S. Li and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shaoqing Wu and Shengfeng Ye and Shengfeng Ye and Shirong Ma and Shiyu Wang and Shuang Zhou and Shuiping Yu and Shunfeng Zhou and Shuting Pan and T. Wang and Tao Yun and Tian Pei and Tianyu Sun and W. L. Xiao and Wangding Zeng and Wanjia Zhao and Wei An and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and X. Q. Li and Xiangyue Jin and Xianzu Wang and Xiao Bi and Xiaodong Liu and Xiaohan Wang and Xiaojin Shen and Xiaokang Chen and Xiaokang Zhang and Xiaosha Chen and Xiaotao Nie and Xiaowen Sun and Xiaoxiang Wang and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xingkai Yu and Xinnan Song and Xinxia Shan and Xinyi Zhou and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and Y. K. Li and Y. Q. Wang and Y. X. Wei and Y. X. Zhu and Yang Zhang and Yanhong Xu and Yanhong Xu and Yanping Huang and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Li and Yaohui Wang and Yi Yu and Yi Zheng and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Ying Tang and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yu Wu and Yuan Ou and Yuchen Zhu and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yukun Zha and Yunfan Xiong and Yunxian Ma and Yuting Yan and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Z. F. Wu and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhen Huang and Zhen Zhang and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhibin Gou and Zhicheng Ma and Zhigang Yan and Zhihong Shao and Zhipeng Xu and Zhiyu Wu and Zhongyu Zhang and Zhuoshu Li and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Ziyi Gao and Zizheng Pan},
      year={2025},
      eprint={2412.19437},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.19437}, 
}

@inproceedings{brown2020language,
  author       = {Tom B. Brown and
                  Benjamin Mann and
                  Nick Ryder and
                  Melanie Subbiah and
                  Jared Kaplan and
                  Prafulla Dhariwal and
                  Arvind Neelakantan and
                  Pranav Shyam and
                  Girish Sastry and
                  Amanda Askell and
                  Sandhini Agarwal and
                  Ariel Herbert{-}Voss and
                  Gretchen Krueger and
                  Tom Henighan and
                  Rewon Child and
                  Aditya Ramesh and
                  Daniel M. Ziegler and
                  Jeffrey Wu and
                  Clemens Winter and
                  Christopher Hesse and
                  Mark Chen and
                  Eric Sigler and
                  Mateusz Litwin and
                  Scott Gray and
                  Benjamin Chess and
                  Jack Clark and
                  Christopher Berner and
                  Sam McCandlish and
                  Alec Radford and
                  Ilya Sutskever and
                  Dario Amodei},
  editor       = {Hugo Larochelle and
                  Marc'Aurelio Ranzato and
                  Raia Hadsell and
                  Maria{-}Florina Balcan and
                  Hsuan{-}Tien Lin},
  title        = {Language Models are Few-Shot Learners},
  booktitle    = {Advances in Neural Information Processing Systems 33: Annual Conference
                  on Neural Information Processing Systems 2020, NeurIPS 2020, December
                  6-12, 2020, virtual},
  year         = {2020},
  url          = {https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html},
  timestamp    = {Thu, 25 May 2023 10:38:31 +0200},
  biburl       = {https://dblp.org/rec/conf/nips/BrownMRSKDNSSAA20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

% ====== 模型引用 ======
@misc{qwen2.5,
      title={Qwen2.5 Technical Report}, 
      author={{Qwen Team}},
      year={2025},
      eprint={2412.15115},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.15115}, 
}

@article{telechat2,
  author       = {Zihan Wang and
                  Xinzhang Liu and
                  Yitong Yao and
                  Chao Wang and
                  Yu Zhao and
                  Zhihao Yang and
                  Wenmin Deng and
                  Kaipeng Jia and
                  Jiaxin Peng and
                  Yuyao Huang and
                  Sishi Xiong and
                  Zhuo Jiang and
                  Kaidong Yu and
                  Xiaohui Hu and
                  Fubei Yao and
                  Ruiyu Fang and
                  Zhuoru Jiang and
                  Ruiting Song and
                  Qiyi Xie and
                  Rui Xue and
                  Xuewei He and
                  Yanlei Xue and
                  Zhu Yuan and
                  Zhaoxi Zhang and
                  Zilu Huang and
                  Shiquan Wang and
                  Xin Wang and
                  Hanming Wu and
                  Mingyuan Wang and
                  Xufeng Zhan and
                  Yuhan Sun and
                  Zhaohu Xing and
                  Yuhao Jiang and
                  Bingkai Yang and
                  Shuangyong Song and
                  Yongxiang Li and
                  Zhongjiang He and
                  Xuelong Li},
  title        = {Technical Report of TeleChat2, TeleChat2.5 and {T1}},
  journal      = {CoRR},
  volume       = {abs/2507.18013},
  year         = {2025},
  url          = {https://doi.org/10.48550/arXiv.2507.18013},
  doi          = {10.48550/ARXIV.2507.18013},
  eprinttype    = {arXiv},
  eprint       = {2507.18013},
  timestamp    = {Mon, 18 Aug 2025 22:03:50 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2507-18013.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
% ====== 提示词 / 推理方法 ======

@misc{liu2025trainingreporttelechat3moe,
      title={Training Report of TeleChat3-MoE},
      author={Xinzhang Liu and Chao Wang and Zhihao Yang and Zhuo Jiang and Xuncheng Zhao and Haoran Wang and Lei Li and Dongdong He and Luobin Liu and Kaizhe Yuan and Han Gao and Zihan Wang and Yitong Yao and Sishi Xiong and Wenmin Deng and Haowei He and Kaidong Yu and Yu Zhao and Ruiyu Fang and Yuhao Jiang and Yingyan Li and Xiaohui Hu and Xi Yu and Jingqi Li and Yanwei Liu and Qingli Li and Xinyu Shi and Junhao Niu and Chengnuo Huang and Yao Xiao and Ruiwen Wang and Fengkai Li and Luwen Pu and Kaipeng Jia and Fubei Yao and Yuyao Huang and Xuewei He and Zhuoru Jiang and Ruiting Song and Rui Xue and Qiyi Xie and Jie Zhang and Zilu Huang and Zhaoxi Zhang and Zhilong Lu and Yanhan Zhang and Yin Zhang and Yanlei Xue and Zhu Yuan and Teng Su and Xin Jiang and Shuangyong Song and Yongxiang Li and Xuelong Li},
      year={2025},
      eprint={2512.24157},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.24157},
}

@inproceedings{kojima2022large,
 author = {Kojima, Takeshi and Gu, Shixiang (Shane) and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {22199--22213},
 publisher = {Curran Associates, Inc.},
 title = {Large Language Models are Zero-Shot Reasoners},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/8bb0d291acd4acf06ef112099c16f326-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

@misc{wang2023selfconsistency,
      title={Self-Consistency Improves Chain of Thought Reasoning in Language Models}, 
      author={Xuezhi Wang and Jason Wei and Dale Schuurmans and Quoc Le and Ed Chi and Sharan Narang and Aakanksha Chowdhery and Denny Zhou},
      year={2023},
      eprint={2203.11171},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2203.11171}, 
}


@misc{zhou2023leasttomost,
      title={Least-to-Most Prompting Enables Complex Reasoning in Large Language Models}, 
      author={Denny Zhou and Nathanael Schärli and Le Hou and Jason Wei and Nathan Scales and Xuezhi Wang and Dale Schuurmans and Claire Cui and Olivier Bousquet and Quoc Le and Ed Chi},
      year={2023},
      eprint={2205.10625},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2205.10625}, 
}

@article{liu2023pretrain,
  author = {Liu, Pengfei and Yuan, Weizhe and Fu, Jinlan and Jiang, Zhengbao and Hayashi, Hiroaki and Neubig, Graham},
  title = {Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing},
  year = {2023},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {55},
  number = {9},
  issn = {0360-0300},
  url = {https://doi.org/10.1145/3560815},
  journal = {ACM Comput. Surv.},
  doi = {10.1145/3560815},
  abstract = {This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x′ that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x̂, from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: It allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this article, we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g.,&nbsp;the choice of pre-trained language models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts but also release other resources, e.g., a website  including constantly updated survey and paperlist.},
  articleno = {195},
  numpages = {35},
  keywords = {Pre-trained language models, prompting}
}

% ====== 知识蒸馏 / 指令微调 ======

@article{hinton2015distilling,
  author       = {Geoffrey E. Hinton and
                  Oriol Vinyals and
                  Jeffrey Dean},
  title        = {Distilling the Knowledge in a Neural Network},
  journal      = {CoRR},
  volume       = {abs/1503.02531},
  year         = {2015},
  url          = {http://arxiv.org/abs/1503.02531},
  eprinttype    = {arXiv},
  eprint       = {1503.02531},
  timestamp    = {Mon, 13 Aug 2018 16:48:36 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/HintonVD15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{ouyang2022training,
  author = {Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and Schulman, John and Hilton, Jacob and Kelton, Fraser and Miller, Luke and Simens, Maddie and Askell, Amanda and Welinder, Peter and Christiano, Paul F and Leike, Jan and Lowe, Ryan},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
  pages = {27730--27744},
  publisher = {Curran Associates, Inc.},
  title = {Training language models to follow instructions with human feedback},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf},
  volume = {35},
  year = {2022}
}

% ====== Related Work 新增文献 (2026-02-28, 需人工核对) ======

@article{rasiah2010evasion,
title = {A framework for the systematic analysis of evasion in parliamentary discourse},
journal = {Journal of Pragmatics},
volume = {42},
number = {3},
pages = {664-680},
year = {2010},
issn = {0378-2166},
doi = {https://doi.org/10.1016/j.pragma.2009.07.010},
url = {https://www.sciencedirect.com/science/article/pii/S0378216609001866},
author = {Parameswary Rasiah},
keywords = {Question Time, Parliamentary questions, Responses, Evasion, Agenda shifts},
abstract = {Evasion (equivocation) has been identified as a matter of concern in political interviews, but no systematic study has been undertaken in the context of parliamentary discourse, notably Question Time, anywhere in the world. This study applies and adapts various approaches on how politicians ‘resist’ answering questions, all of which are based on political news interviews, to the study of evasion in parliamentary discourse. A comprehensive, unified framework for the analysis of evasion is described, a decision flow-chart for the framework is provided, and an illustrative example of the applied framework is given based on Australia's Question Time. It involved the classification of responses as ‘answers’, ‘intermediate responses’, and ‘evasions’ based on specific criteria. Responses which were considered evasions were further analysed to determine the levels of evasion, whether they were covert or overt in nature and the types of ‘agenda shifts’ that occurred, if any. The corpus investigated consists of Australia's House of Representatives’ Question Time transcripts, for the months of February and March 2003, on the specific issue of Iraq. The study provides empirical evidence that evasion does occur during parliamentary Question Time.}
}

@inproceedings{yao2023tree,
 author = {Yao, Shunyu and Yu, Dian and Zhao, Jeffrey and Shafran, Izhak and Griffiths, Tom and Cao, Yuan and Narasimhan, Karthik},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
 pages = {11809--11822},
 publisher = {Curran Associates, Inc.},
 title = {Tree of Thoughts: Deliberate Problem Solving with Large Language Models},
 url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/271db9922b8d1f4dd7aaef84ed5ac703-Paper-Conference.pdf},
 volume = {36},
 year = {2023}
}


@inproceedings{khattab2024dspy,
  title={DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines},
  author={Khattab, Omar and Singhvi, Arnav and Maheshwari, Paridhi and Zhang, Zhiyuan and Santhanam, Keshav and Vardhamanan, Sri and Haq, Saiful and Sharma, Ashutosh and Joshi, Thomas T. and Moazam, Hanna and Miller, Heather and Zaharia, Matei and Potts, Christopher},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/forum?id=sY5N0zY5Od}
}


@inproceedings{wang2023planandsolve,
    title = "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models",
    author = "Wang, Lei  and
      Xu, Wanyu  and
      Lan, Yihuai  and
      Hu, Zhiqiang  and
      Lan, Yunshi  and
      Lee, Roy Ka-Wei  and
      Lim, Ee-Peng",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.147/",
    doi = "10.18653/v1/2023.acl-long.147",
    pages = "2609--2634",
    abstract = "Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, Few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy. To eliminate the manual efforts, Zero-shot-CoT concatenates the target problem statement with ``\textit{Let{'}s think step by step}'' as an input prompt to LLMs. Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors. To address the missing-step errors, we propose Plan-and-Solve (PS) Prompting. It consists of two components: first, devising a plan to divide the entire task into smaller subtasks, and then carrying out the subtasks according to the plan. To address the calculation errors and improve the quality of generated reasoning steps, we extend PS prompting with more detailed instructions and derive PS+ prompting. We evaluate our proposed prompting strategy on ten datasets across three reasoning problems. The experimental results over GPT-3 show that our proposed zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought Prompting, and has comparable performance with 8-shot CoT prompting on the math reasoning problem. The code can be found at \url{https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting}."
}

@article{uma2021learning,
title = "Learning from Disagreement: A Survey",
abstract = "Many tasks in Natural Language Processing (nlp) and Computer Vision (cv) offer ev- idence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition follows from certain premises. While most learning in artificial intelligence (ai) still relies on the as- sumption that a single (gold) interpretation exists for each item, a growing body of research aims to develop learning methods that do not rely on this assumption. In this survey, we review the evidence for disagreements on nlp and cv tasks, focusing on tasks for which substantial datasets containing this information have been created. We discuss the most popular approaches to training models from datasets containing multiple judgments poten- tially in disagreement. We systematically compare these different approaches by training them with each of the available datasets, considering several ways to evaluate the resulting models. Finally, we discuss the results in depth, focusing on four key research questions, and assess how the type of evaluation and the characteristics of a dataset determine the answers to these questions. Our results suggest, first of all, that even if we abandon the assumption of a gold standard, it is still essential to reach a consensus on how to evaluate models. This is because the relative performance of the various training methods is crit- ically affected by the chosen form of evaluation. Secondly, we observed a strong dataset effect. With substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from ag- gregated or even gold labels. This result holds for both hard and soft evaluation. But when the above conditions do not hold, leveraging both gold and soft labels generally achieved the best results in the hard evaluation. All datasets and models employed in this paper are freely available as supplementary materials.",
keywords = "uncertainty, machine learning, natural language, vision, uncertainty, machine learning, natural language, vision",
author = "Alexandra Uma and Tommaso Fornaciari and Dirk Hovy and Silviu Paun and Barbara Plank and Massimo Poesio",
year = "2021",
month = dec,
day = "27",
doi = "10.1613/jair.1.12752",
language = "English",
volume = "72",
pages = "1385--1470",
journal = "The Journal of Artificial Intelligence Research",
issn = "1076-9757",
publisher = "AAAI Press",
}

@inproceedings{dong2024icl,
    title = "A Survey on In-context Learning",
    author = "Dong, Qingxiu  and
      Li, Lei  and
      Dai, Damai  and
      Zheng, Ce  and
      Ma, Jingyuan  and
      Li, Rui  and
      Xia, Heming  and
      Xu, Jingjing  and
      Wu, Zhiyong  and
      Chang, Baobao  and
      Sun, Xu  and
      Li, Lei  and
      Sui, Zhifang",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.64/",
    doi = "10.18653/v1/2024.emnlp-main.64",
    pages = "1107--1128",
    abstract = "With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make predictions based on contexts augmented with a few examples. It has been a significant trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, prompt designing strategies, and related analysis. Additionally, we explore various ICL application scenarios, such as data engineering and knowledge updating. Finally, we address the challenges of ICL and suggest potential directions for further research. We hope that our work can encourage more research on uncovering how ICL works and improving ICL."
}

@inproceedings{diao2023active,
    title = "Active Prompting with Chain-of-Thought for Large Language Models",
    author = "Diao, Shizhe  and
      Wang, Pengcheng  and
      Lin, Yong  and
      Pan, Rui  and
      Liu, Xiang  and
      Zhang, Tong",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.73/",
    doi = "10.18653/v1/2024.acl-long.73",
    pages = "1330--1350",
    abstract = "The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs' ability to produce high-quality answers. In particular, an effective approach for complex question-and-answering tasks is example-based prompting with chain-of-thought (CoT) reasoning, which significantly improves the performance of LLMs. However, current CoT methods rely on a fixed set of human-annotated exemplars, which are not necessarily the most effective examples for different tasks. This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning). For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful to annotate from a pool of task-specific queries. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty so as to select the most uncertain questions for annotation. Experimental results demonstrate the superiority of our proposed method, achieving superior performance on eight complex reasoning tasks. Further analyses of different uncertainty metrics, pool sizes, zero-shot learning, and accuracy-uncertainty relationships demonstrate the effectiveness of our method."
}
% ====== 标注分歧与政治话语（原有） ======

@inproceedings{plank2014linguistically,
  title = {Linguistically debatable or just plain wrong?},
  author = {Plank, Barbara and Hovy, Dirk and S{\o}gaard, Anders},
  editor = {Toutanova, Kristina and Wu, Hua},
  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month = jun,
  year = {2014},
  address = {Baltimore, Maryland},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/P14-2083/},
  doi = {10.3115/v1/P14-2083},
  pages = {507--511},
}

@article{bull1993microanalysis,
  title={On Identifying Questions, Replies, and Non-Replies in Political Interviews},
  volume={13},
  ISSN={1552-6526},
  url={http://dx.doi.org/10.1177/0261927X94132002},
  DOI={10.1177/0261927x94132002},
  number={2},
  journal={Journal of Language and Social Psychology},
  publisher={SAGE Publications},
  author={Bull, Peter},
  year={1994},
  month=jun,
  pages={115--131}
}

% ====== LoRA / SFT 工具 ======

@inproceedings{hu2022lora,
  title={{LoRA}: Low-Rank Adaptation of Large Language Models},
  author={Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=nZeVKeeFYf9}
}

@inproceedings{zheng2024llamafactory,
    title = "{L}lama{F}actory: Unified Efficient Fine-Tuning of 100+ Language Models",
    author = "Zheng, Yaowei  and
      Zhang, Richong  and
      Zhang, Junhao  and
      Ye, Yanhan  and
      Luo, Zheyan",
    editor = "Cao, Yixin  and
      Feng, Yang  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-demos.38/",
    doi = "10.18653/v1/2024.acl-demos.38",
    pages = "400--410",
     abstract = "Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It provides a solution for flexibly customizing the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and received over 25,000 stars and 3,000 forks."
}

% ====== Vicinagearth期刊引用（已验证修正）======
% 添加日期: 2026-03-26

% 1. AI Flow - 必须引用（公司要求）
@article{an2026aiflow,
  title={AI Flow: perspectives, scenarios, and approaches},
  author={An, Hongjun and Hu, Wenhan and Huang, Sida and Huang, Siqi and Li, Ruanjun and Liang, Yuanzhi and Shao, Jiawei and Song, Yiliang and Wang, Zihan and Yuan, Cheng and Zhang, Chi and Zhang, Hongyuan and Zhuang, Wenhao and Li, Xuelong},
  journal={Vicinagearth},
  year={2026},
  volume={3},
  number={1},
  doi={10.1007/s44336-025-00031-y},
  url={https://doi.org/10.1007/s44336-025-00031-y},
  publisher={Springer Nature}
}

% 2. Video Object Segmentation Survey
@article{shen2025videoseg,
  title={A survey of language-guided video object segmentation: from referring to reasoning},
  author={Shen, Yiqing and Zhang, Dell},
  journal={Vicinagearth},
  volume={2},
  number={1},
  articleno={9},
  year={2025},
  publisher={Springer Nature},
  doi={10.1007/s44336-025-00018-9},
  url={https://link.springer.com/article/10.1007/s44336-025-00018-9}
}

% 3. Multi-intent SLU Survey
@article{wu2025multislu,
  title={Multi-intent spoken language understanding: a survey of methods, trends, and challenges},
  author={Wu, Di and Fang, Ruiyu and Jiang, Liting and Song, Shuangyong and Huang, Xiaomeng and Wang, Shiquan and Li, Zhongqiu and Shi, Lingling and Bao, Mengjiao and Li, Yongxiang and Huang, Hao},
  journal={Vicinagearth},
  volume={2},
  number={1},
  articlenumber={20},
  year={2025},
  publisher={Springer Nature},
  doi={10.1007/s44336-025-00029-6},
  url={https://link.springer.com/article/10.1007/s44336-025-00029-6}
}

% 4. Tool Learning Survey
@article{chen2025tool,
  title={Tool learning with language models: a comprehensive survey of methods, pipelines, and benchmarks},
  author={Chen, Jinyang and Wu, Haolun and Pang, Jianhong and Wang, Yihua and Zhang, Dell and Sun, Changzhi},
  journal={Vicinagearth},
  volume={2},
  number={1},
  article-number={16},
  year={2025},
  publisher={Springer Nature},
  doi={10.1007/s44336-025-00024-x},
  url={https://doi.org/10.1007/s44336-025-00024-x}
}

% 5. NL2SQL Survey
@article{fan2025nl2sql,
  author={Fan, Yuankai and Weng, Qizhen and Chen, Yin and Wang, X. Sean},
  title={Rethinking Data in {NL2SQL}: A Survey of What We Have and What We Expect},
  journal={Vicinagearth},
  volume={2},
  number={1},
  pages={15},
  year={2025},
  publisher={Springer Nature},
  doi={10.1007/s44336-025-00026-9},
  url={https://doi.org/10.1007/s44336-025-00026-9}
}

% ====== TeleChat 系列技术报告 ======

@misc{telechat1,
  title={TeleChat Technical Report},
  author={Zihan Wang and Xinzhang Liu and Shixuan Liu and Yitong Yao and Yuyao Huang and Zhongjiang He and Xuelong Li and others},
  year={2024},
  eprint={2401.03804},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2401.03804}
}

@inproceedings{telechat_acl,
  title={{TeleChat}: An Open-source Bilingual Large Language Model},
  author={Zihan Wang and Xinzhang Liu and Shixuan Liu and Yitong Yao and others},
  booktitle={Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)},
  year={2024},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/2024.sighan-1.2/}
}

@misc{teleflm,
  title={Tele-{FLM} Technical Report},
  author={Xiang Li and Yiqun Yao and Xin Jiang and Xuezhi Fang and Chao Wang and Xinzhang Liu and Zihan Wang and Yu Zhao and Xin Wang and Yuyao Huang and Shuangyong Song and Yongxiang Li and Zhongjiang He and Xuelong Li and others},
  year={2024},
  eprint={2404.16645},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2404.16645}
}

@misc{teleflm1t,
  title={52{B} to 1{T}: Lessons Learned via {Tele-FLM} Series},
  author={Yiqun Yao and Xiang Li and Xin Jiang and others},
  year={2024},
  eprint={2407.02783},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2407.02783}
}

% ====== CoT 相关引用 ======

@misc{lemur2024,
  title={Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging},
  author={Wei Zhang and Hongcheng Guo and Jian Yang and others},
  year={2024},
  eprint={2402.18205},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2402.18205}
}

@misc{chainoflure,
  title={Chain-of-Lure: A Synthetic Narrative-Driven Approach to Compromise Large Language Models},
  author={Wenhan Chang and Tianrui Zhu and Yue Zhao and Shuangyong Song and Peng Xiong and Wenhao Zhou and Yongxiang Li},
  year={2025},
  eprint={2505.17519},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2505.17519}
}

% ====== Reasoning 相关引用 ======

@inproceedings{cemt2026,
  title={{CEMT}: Chain-of-Thought Enhanced Machine Translation},
  author={Lingling Shi and Haoyu Jin and Ruiyu Fang and Shuangyong Song and Jinsong Su and Yongxiang Li and Xuelong Li},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2026},
  year={2026},
  publisher={Association for Computational Linguistics}
}

@article{mruie,
  title={{MR-UIE}: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction},
  author={Zhongqiu Li and Shiquan Wang and Ruiyu Fang and Mengjiao Bao and Zhenhe Wu and Shuangyong Song and Yongxiang Li and Xuelong Li},
  journal={Vicinagearth},
  volume={2},
  number={1},
  year={2025},
  publisher={Springer Nature},
  doi={10.1007/s44336-025-00019-8},
  url={https://link.springer.com/article/10.1007/s44336-025-00019-8}
}

@misc{tablereasoner,
  title={{TableReasoner}: Advancing Table Reasoning Framework with Large Language Models},
  author={Sishi Xiong and Dakai Wang and Yu Zhao and Jie Zhang and Changzai Pan and Haowei He and Xiangyu Li and Wenhan Chang and Xuelong Li and others},
  year={2025},
  eprint={2507.08046},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2507.08046}
}

@article{mathclg,
  title={Enhancing Math Reasoning Ability of Large Language Models via Computation Logic Graphs},
  author={Okwuonu, Uche Christina and Njoya, Helen Kwipnchep and Peremoboere, Uzoigwe Tracy},
  journal={Knowledge-Based Systems},
  year={2025},
  doi={10.1016/j.knosys.2025.113905},
  publisher={Elsevier},
  url={https://www.sciencedirect.com/science/article/pii/S0950705125009517}
}

@inproceedings{llmsr_xllm25,
  title={{LLMSR}@{XLLM25}: A Language Model-Based Pipeline for Structured Reasoning Data Construction},
  author={Hongrui Xing and Xinzhang Liu and Zhuo Jiang and Zhihao Yang and Yitong Yao and Zihan Wang and others},
  booktitle={Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)},
  year={2025},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/2025.xllm-1.31/}
}

@inproceedings{tablereasoner_semeval,
  title={{TeleAI} at {SemEval}-2025 Task 8: Advancing Table Reasoning Framework with Large Language Models},
  author={Sishi Xiong and Dakai Wang and Yu Zhao and Jie Zhang and Changzai Pan and Haowei He and Xiangyu Li and Wenhan Chang and Xuelong Li and others},
  booktitle={Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)},
  year={2025},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/2025.semeval-1.240/}
}

% ====== CLARITY Task Description Paper (Required by organizers) ======
@misc{thomas2026semeval2026task6clarity,
    title={SemEval-2026 Task 6: {CLARITY} -- Unmasking Political Question Evasions},
    author={Konstantinos Thomas and Giorgos Filandrianos and Maria Lymperaiou and Chrysoula Zerva and Giorgos Stamou},
    year={2026},
    eprint={2603.14027},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2603.14027},
}