Elamur Research⌕
☰
home / papers / 2025 / Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs

Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs

Paper · 2025 · Yu Xia, Rui Wang, Xu Liu et al.

Summary

Chain-of-Thought (CoT) prompting showed that LLMs reason better when they write out intermediate steps before giving an answer. Many later methods kept this sequential idea but built the chain from other kinds of elements, and no prior work had organized this growing family. Xia et al. (COLING 2025) address that gap with a survey of "Chain-of-X" (CoX) approaches. They formalize CoX as a generalization of CoT: an input, a sequence of nodes X1…Xn, and an output. The nodes either combine to build a solution or refine a draft step by step.

Earlier CoT surveys concentrate on the structure of reasoning thoughts. This survey instead classifies methods along two axes: what kind of node forms the chain, and which task the chain serves. It names representative works for each category and gives a fuller catalogue in an appendix. The authors close with open problems, including how intermediate steps causally affect answers and how costly long chains are. The result is a reference map for researchers who want to apply the CoT idea to problems beyond pure reasoning.

Key points

  • Four node types:
    • Chain-of-Intermediates, split into problem decomposition (e.g., Least-to-Most, Chain-of-Code, Chain-of-Table) and knowledge composition (e.g., Chain-of-Knowledge, Chain-of-Spot).
    • Chain-of-Augmentation, using instructions, histories, retrievals (e.g., ReAct), tools, or others.
    • Chain-of-Feedback, either external or self-generated (e.g., Self-Refine, Chain-of-Verification).
    • Chain-of-Models (e.g., ChatEval, Chain-of-Discussion).
  • Six task areas:
    • Multimodal interaction (text–image, text–table, text–code, text–speech).
    • Factuality & safety (hallucination, alignment).
    • Multi-step reasoning.
    • Instruction following (e.g., Chain-of-LoRA, which routes each instruction type to a task-specific LoRA network).
    • LLMs as agents.
    • Evaluation tools.
  • Evaluation-oriented CoX: BadChain exposes backdoor vulnerabilities in CoT prompting. Chain-of-Feedback (Ahn and Shin) shows that answer quality degrades under repeated uninformative "try again" prompts.
  • Four future directions:
    • Causal analysis of intermediate nodes (e.g., CoT still helps even with invalid rationales).
    • Lowering inference cost through shorter or parallel chains.
    • Distilling CoX outputs into smaller student models.
    • End-to-end fine-tuning to stop errors from accumulating along the chain.
  • Publication: Proceedings of COLING 2025, pp. 10795–10809. The authors are from Shanghai Jiao Tong University, UC San Diego, Duke University and Adobe Research.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Shanghai Jiao Tong UniversityShanghai · China149 works · 16,161 citations🎓University of California San DiegoSan Diego · United States73 works · 51,948 citations🎓Duke UniversityDurham · United States28 works · 15,077 citations🏢Adobe SystemsSan Jose · United States35 works · 3,711 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley and Shuai Li. 2025. Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{xia2025beyond,
  title = {{Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs}},
  author = {Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley and Shuai Li},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.719/}
}

References

104 references, 31 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Griffin Adams, Alex Fabbri, Faisal Ladhak, Eric Lehman, and Noémie Elhadad. 2023. From sparse to dense: GPT-4 summarization with chain of density prompting. In Proceedings of the 4th New Frontiers in Summarization Workshop, pages 68–74, Singapore. Association for Computational Linguistics. find
  2. Garima Agrawal, Tharindu Kumarage, Zeyad Alghami, and Huan Liu. 2023. Can knowledge graphs reduce hallucinations in llms?: A survey. arXiv preprint arXiv:2311.07914. find
  3. Jinwoo Ahn and Kyuseung Shin. 2024. Recursive chain-of-feedback prevents performance degradation from redundant prompting. arXiv preprint arXiv:2402.02648v2. — [arXiv:2402.02648](https://arxiv.org/abs/2402.02648) find
  4. Songlin Bao, Tiantian Li, and Bin Cao. 2024. Chain-of-event prompting for multi-document summarization by large language models. International Journal of Web Information Systems. find
  5. Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, et al. 2024. Topologies of reasoning: Demystifying chains, trees, and graphs of thoughts. arXiv preprint arXiv:2401.14295. — [arXiv:2401.14295](https://arxiv.org/abs/2401.14295) find
  6. Rishabh Bhardwaj and Soujanya Poria. 2023. Red-teaming large language models using chain of utterances for safety-alignment. arXiv preprint arXiv:2308.09662. find
  7. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023a. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. Transactions on Machine Learning Research. find
  8. Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong, Xin Zhao, and Ji-Rong Wen. 2023b. ChatCoT: Tool-augmented chain-of-thought reasoning on chat-based large language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14777–14790, Singapore. Association for Computa… find
  9. Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, and Ting Liu. 2024. Navigate through enigmatic labyrinth a survey of chain of thought reasoning: Advances, frontiers and future. In The 62nd Annual Meeting of the Association for Computatio… find
  10. Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023. Chain-of-verification reduces hallucination in large language models. arXiv preprint arXiv:2309.11495.
  11. Xuan Long Do, Kenji Kawaguchi, Min Yen Kan, and Nancy F Chen. 2023. Choire: Characterizing and predicting human opinions with chain of opinion reasoning. arXiv preprint arXiv:2311.08385.
  12. Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socol-insky, Srinivasan Sengamedu, and Christos Faloutsos. 2024. Large language models(llms) on tabular data: Prediction, generation, and understanding – a survey. arXiv preprint arXiv:2402.17944. — [arXiv:2402.17944](https://arxiv.org/abs/2402.17944) find
  13. Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, and Tianlu Wang. 2024. Efficient tool use with chain-of-abstraction reasoning. arXiv preprint arXiv:2401.17464.
  14. Peiyuan Gong and Jiaxin Mao. 2023. Coascore: Chain-of-aspects prompting for nlg evaluation. arXiv preprint arXiv:2312.10355. — [arXiv:2312.10355](https://arxiv.org/abs/2312.10355) find
  15. Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. 2024. Chateval: Towards better LLM-based evaluators through multi-agent debate. In The Twelfth International Conference on Learning Representations.
  16. Guangzeng Han, Weisi Liu, Xiaolei Huang, and Brian Borsari. 2024. Chain-of-interaction: Enhancing large language models for psychiatric behavior understanding by dyadic contexts. arXiv preprint arXiv:2403.13786. — [arXiv:2403.13786](https://arxiv.org/abs/2403.13786) find
  17. Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2023. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology.
  18. Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long, Sudipta Kar, Abhinav Sethy, Joo-Kyung Kim, and Dongyeop Kang. 2024. Chain-of-instructions: Compositional instruction tuning on large language models. arXiv preprint arXiv:2402.11532. — [arXiv:2402.11532](https://arxiv.org/abs/2402.11532) find
  19. Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. In Findings of the Association for Computati…
  20. Chenxu Hu, Jie Fu, Chenzhuang Du, Simian Luo, Junbo Zhao, and Hang Zhao. 2023a. Chatdb: Augmenting llms with databases as their symbolic memory. arXiv preprint arXiv:2306.03901. — [arXiv:2306.03901](https://arxiv.org/abs/2306.03901) find
  21. Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations.
  22. Hanxu Hu, Hongyuan Lu, Huajian Zhang, Wai Lam, and Yue Zhang. 2023b. Chain-of-symbol prompting elicits planning in large langauge models. arXiv preprint arXiv:2305.10276. — [arXiv:2305.10276](https://arxiv.org/abs/2305.10276) find
  23. Fan Huang, Haewoon Kwak, and Jisun An. 2023. Chain of explanation: New prompting method to generate quality natural language explanation for implicit hate speech. In Companion Proceedings of the ACM Web Conference 2023, pages 90–93. find
  24. Jie Huang and Kevin Chen-Chuan Chang. 2023. Towards reasoning in large language models: A survey. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1049–1065, Toronto, Canada. Association for Computational Linguistics.
  25. Qiang Huang, Feng Huang, DeHao Tao, YueTong Zhao, BingKun Wang, and YongFeng Huang. 2024. Coq: An empirical framework for multi-hop question answering empowered by large language models. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1156… find
  26. Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, and Sadao Kurohashi. 2023. MultiTool-CoT: GPT-3 can use multiple external tools with chain of thought prompting. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1522–1532, Toronto,… find
  27. Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Suhang Wang, Yu Meng, and Jiawei Han. 2024. Graph chain-of-thought: Augmenting large language models by reasoning on graphs. arXiv preprint arXiv:2404.07103.
  28. Taehee Kim, Yeongjae Cho, Heejun Shin, Yohan Jo, and Dongmyung Shin. 2024. Generalizing visual question answering from synthetic to human-written questions via a chain of qa with a large language model. arXiv preprint arXiv:2401.06400. — [arXiv:2401.06400](https://arxiv.org/abs/2401.06400) find
  29. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199– 22213.
  30. Aditya Kuppa, Nikon Rasumov-Rahe, and Marc Voses. 2023. Chain of reference prompting helps llm to think like a lawyer. In Generative AI+ Law Workshop. find
  31. Emanuele La Malfa, Christoph Weinhuber, Orazio Torre, Fangru Lin, Anthony Cohn, Nigel Shadbolt, and Michael Wooldridge. 2024. Code simulation challenges for large language models. arXiv preprint arXiv:2401.09074. — [arXiv:2401.09074](https://arxiv.org/abs/2401.09074) find
  32. Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo, and Shafiq Joty. 2024. Codechain: Towards modular code generation through chain of self-revisions with representative sub-modules. In The Twelfth International Conference on Learning Representations. find
  33. Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi. 2023a. Rlaif: Scaling reinforcement learning from human feedback with ai feedback. arXiv preprint arXiv:2309.00267. find
  34. Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, and Sowon Hahn. 2023b. Chain of empathy: Enhancing empathetic response of large language models based on psychotherapy models. arXiv preprint arXiv:2311.04915. — [arXiv:2311.04915](https://arxiv.org/abs/2311.04915) find
  35. Deren Lei, Yaxi Li, Mingyu Wang, Vincent Yun, Emily Ching, Eslam Kamal, et al. 2023. Chain of natural language inference for reducing large language model ungrounded hallucinations. arXiv preprint arXiv:2310.03951. — [arXiv:2310.03951](https://arxiv.org/abs/2310.03951) find
  36. Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, and Brian Ichter. 2023a. Chain of code: Reasoning with a language model-augmented code emulator. arXiv preprint arXiv:2312.04474. — [arXiv:2312.04474](https://arxiv.org/abs/2312.04474) find
  37. Liunian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai-Wei Chang, and Yejin Choi. 2023b. Symbolic chain-of-thought distillation: Small models can also “think” step-by-step. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages…
  38. Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, and Lidong Bing. 2024a. Chain-of-knowledge: Grounding large language models via dynamic knowledge adapting over heterogeneous sources. In The Twelfth International Conference on Learning Representations. find
  39. Yangning Li, Shirong Ma, Xiaobin Wang, Shen Huang, Chengyue Jiang, Hai-Tao Zheng, Pengjun Xie, Fei Huang, and Yong Jiang. 2024b. Ecomgpt: Instruction-tuning large language models with chain-of-task tasks for e-commerce. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pag… find
  40. Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. 2023. Deductive verification of chain-of-thought reasoning. In Thirty-seventh Conference on Neural Information Processing Systems.
  41. Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023. Self-refine: Iterative refinement with self-feedback. In Thirty-seventh Conference on Neural Information Processing Systems. find
  42. Fanxu Meng, Haotong Yang, Yiding Wang, and Muhan Zhang. 2023. Chain of images for intuitively reasoning. arXiv preprint arXiv:2311.09241. — [arXiv:2311.09241](https://arxiv.org/abs/2311.09241) find
  43. Hanmeng Liu, Zhiyang Teng, Leyang Cui, Chaoli Zhang, Qiji Zhou, and Yue Zhang. 2023. LogiCoT: Logical chain-of-thought instruction tuning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2908–2921, Singapore. Association for Computational Linguistics. find
  44. Chancharik Mitra, Brandon Huang, Trevor Darrell, and Roei Herzig. 2024. Compositional chain of thought prompting for large multimodal models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). find
  45. Hao Liu, Carmelo Sferrazza, and Pieter Abbeel. 2024a. Chain of hindsight aligns language models with feedback. In The Twelfth International Conference on Learning Representations. find
  46. Debjyoti Mondal, Suraj Modi, Subhadarshi Panda, Rituraj Singh, and Godawari Sudhakar Rao. 2024. Kam-cot: Knowledge augmented multimodal chain-of-thoughts reasoning. In AAAI Conference on Artificial Intelligence. find
  47. Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, and James T. Kwok. 2024b. Mixture of insightful experts (mote): The synergy of thought chains and expert mixtures in self-alignment. arXiv preprint arXiv:2405.00557. — [arXiv:2405.00557](https://arxiv.org/abs/2405.00557) find
  48. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730–27744.
  49. Zuyan Liu, Yuhao Dong, Yongming Rao, Jie Zhou, and Jiwen Lu. 2024c. Chain-of-spot: Interactive reasoning improves large vision-language models. arXiv preprint arXiv:2403.12966. — [arXiv:2403.12966](https://arxiv.org/abs/2403.12966) find
  50. Hongyuan Lu, Haoyang Huang, Dongdong Zhang, Haoran Yang, Wai Lam, and Furu Wei. 2023. Chain-of-dictionary prompting elicits translation in large language models. arXiv preprint arXiv:2305.06575.
  51. Ruilin Luo, Tianle Gu, Haoling Li, Junzhe Li, Zicheng Lin, Jiayi Li, and Yujiu Yang. 2024. Chain of history: Learning and forecasting with llms for temporal knowledge graph completion. arXiv preprint arXiv:2401.06072. find
  52. Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023. Faithful chain-of-thought reasoning. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter…
  53. Weiyu Ma, Qirui Mi, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, and Jun Wang. 2023. Large language models play starcraft ii: Benchmarks and a chain of summarization approach. arXiv preprint arXiv:2312.11865. — [arXiv:2312.11865](https://arxiv.org/abs/2312.11865) find
  54. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Zhenyu Pan, Haozheng Luo, Manling Li, and Han Liu. 2024. Chain-of-action: Faithful and multimodal question answering through large language models. arXiv preprint arXiv:2403.17359. — [arXiv:2403.17359](https://arxiv.org/abs/2403.17359) find
  55. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah Smith, and Mike Lewis. 2023. Measuring and narrowing the compositionality gap in language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5687–5711, Singapore. Association for Computational Linguistics. find
  56. Ji Qi, Ming Ding, Weihan Wang, Yushi Bai, Qingsong Lv, Wenyi Hong, Bin Xu, Lei Hou, Juanzi Li, Yuxiao Dong, et al. 2024. Cogcom: Train large vision-language models diving into details through chain of manipulations. arXiv preprint arXiv:2402.04236. — [arXiv:2402.04236](https://arxiv.org/abs/2402.04236) find
  57. Xihe Qiu, Teqi Hao, Shaojie Shi, Xiaoyu Tan, and Yu-Jie Xiong. 2024. Chain-of-lora: Enhancing the instruction fine-tuning performance of low-rank adaptation on diverse instruction set. IEEE Signal Processing Letters. find
  58. Sergio Servantez, Joe Barrow, Kristian Hammond, and Rajiv Jain. 2024. Chain of logic: Rule-based reasoning with large language models. arXiv preprint arXiv:2402.10400. find
  59. Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li. 2024. Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models. arXiv preprint arXiv:2403.16999. find
  60. Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel Ni, Heung-Yeung Shum, and Jian Guo. 2024. Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. In The Twelfth International Conference on Learning Representations. find
  61. Mingxu Tao, Dongyan Zhao, and Yansong Feng. 2024. Chain-of-discussion: A multi-model framework for complex evidence-based question answering. arXiv preprint arXiv:2402.16313. find
  62. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p…
  63. Kohei Uehara, Nabarun Goswami, Hanqin Wang, Toshiaki Baba, Kohtaro Tanaka, Tomohiro Hashimoto, Kai Wang, Rei Ito, Takagi Naoya, Ryo Umagami, et al. 2024. Advancing large multi-modal models with explicit chain-of-reasoning and visual question generation. arXiv preprint arXiv:2401.10005. — [arXiv:2401.10005](https://arxiv.org/abs/2401.10005) find
  64. Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun. 2023a. Towards understanding chain-of-thought prompting: An empirical study of what matters. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pa…
  65. Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023g. Aligning large language models with human: A survey. arXiv preprint arXiv:2307.12966.
  66. Zihao Wang, Anji Liu, Haowei Lin, Jiaqi Li, Xiaojian Ma, and Yitao Liang. 2024a. Rat: Retrieval augmented thoughts elicit context-aware reasoning in long-horizon generation. arXiv preprint arXiv:2403.05313.
  67. Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, and Tomas Pfister. 2024b. Chain-of-table: Evolving tables in the reasoning chain for table understanding. In The Twelfth International Conference… find
  68. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022. Chain of thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems.
  69. Nan Xi, Jingjing Meng, and Junsong Yuan. 2023a. Chain-of-look prompting for verb-centric surgical triplet recognition in endoscopic videos. In Proceedings of the 31st ACM International Conference on Multimedia, pages 5007–5016. find
  70. Hanbin Wang, Zhenghao Liu, Shuo Wang, Ganqu Cui, Ning Ding, Zhiyuan Liu, and Ge Yu. 2023b. Intervenor: Prompt the coding ability of large language models with the interactive chain of repairing. arXiv preprint arXiv:2311.09868. — [arXiv:2311.09868](https://arxiv.org/abs/2311.09868) find
  71. Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al. 2023b. The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864.
  72. Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, and Kam-Fai Wong. 2023c. Cue-CoT: Chain-of-thought prompting for responding to in-depth dialogue questions with LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 12047–12064, Singapore.… find
  73. Wenhan Xia, Chengwei Qin, and Elad Hazan. 2024a. Chain of lora: Efficient fine-tuning of language models via residual learning. arXiv preprint arXiv:2401.04151. — [arXiv:2401.04151](https://arxiv.org/abs/2401.04151) find
  74. Jianing Wang, Qiushi Sun, Nuo Chen, Xiang Li, and Ming Gao. 2023d. Boosting language models reasoning with chain-of-knowledge prompting. arXiv preprint arXiv:2306.06427.
  75. Keheng Wang, Feiyu Duan, Sirui Wang, Peiguang Li, Yunsen Xian, Chuantao Yin, Wenge Rong, and Zhang Xiong. 2023e. Knowledge-driven cot: Exploring faithful reasoning in llms for knowledge-intensive question answering. arXiv preprint arXiv:2308.13259. — [arXiv:2308.13259](https://arxiv.org/abs/2308.13259) find
  76. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023f. Self-consistency improves Yu Xia, Fang Kong, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, and Shuai Li. 2024b. Which llm to play? convergence-aware online model selection with… find
  77. Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan Rossi, Anup Rao, Tung Mai, and Shuai Li. 2024c. Hallucination diversity-aware active learning for text summarization. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techn… find
  78. Yu Xia, Tong Yu, Zhankui He, Handong Zhao, Julian McAuley, and Shuai Li. 2024d. Aligning as debiasing: Causality-aware alignment via reinforcement learning with interventional feedback. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguisti… find
  79. Yuwei Xia, Ding Wang, Qiang Liu, Liang Wang, Shu Wu, and Xiaoyu Zhang. 2024e. Enhancing temporal knowledge graph forecasting with large language models via chain-of-history reasoning. arXiv preprint arXiv:2402.14382. — [arXiv:2402.14382](https://arxiv.org/abs/2402.14382) find
  80. Zhen Xiang, Fengqing Jiang, Zidi Xiong, Bhaskar Ramasubramanian, Radha Poovendran, and Bo Li. 2024. Badchain: Backdoor chain-of-thought prompting for large language models. In The Twelfth International Conference on Learning Representations. find
  81. Zeqi Xiao, Tai Wang, Jingbo Wang, Jinkun Cao, Wenwei Zhang, Bo Dai, Dahua Lin, and Jiangmiao Pang. 2024a. Unified human-scene interaction via prompted chain-of-contacts. In The Twelfth International Conference on Learning Representations. find
  82. Ziyang Xiao, Dongxiang Zhang, Yangjun Wu, Lilin Xu, Yuan Jessica Wang, Xiongwei Han, Xiaojin Fu, Tao Zhong, Jia Zeng, Mingli Song, and Gang Chen. 2024b. Chain-of-experts: When LLMs meet complex operations research problems. In The Twelfth International Conference on Learning Representations. find
  83. Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-Seng Chua. 2024. Search-in-the-chain: Interactively enhancing large language models with search for knowledge-intensive tasks. In Proceedings of the ACM on Web Conference 2024, pages 1362–1373. find
  84. Yutaro Yamada, Khyathi Chandu, Yuchen Lin, Jack Hessel, Ilker Yildirim, and Yejin Choi. 2024. L3go: Language agents with chain-of-3d-thoughts for generating unconventional objects. arXiv preprint arXiv:2402.09052. find
  85. Tao Yang, Tianyuan Shi, Fanqi Wan, Xiaojun Quan, Qifan Wang, Bingzhe Wu, and Jiaxiang Wu. 2023. PsyCoT: Psychological questionnaire as powerful chain-of-thought for personality detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3305–3320, Singapore. Associatio… find
  86. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2023. React: Synergizing reasoning and acting in language models. In The Eleventh International Conference on Learning Representations.
  87. Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. 2023a. Chain-of-note: Enhancing robustness in retrieval-augmented language models. arXiv preprint arXiv:2311.09210.
  88. Zihan Yu, Liang He, Zhen Wu, Xinyu Dai, and Jiajun Chen. 2023b. Towards better chain-of-thought prompting strategies: A survey. arXiv preprint arXiv:2310.04959. — [arXiv:2310.04959](https://arxiv.org/abs/2310.04959) find
  89. Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou. 2023. Large language models meet NL2Code: A survey. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7443– 7464, Toronto,… find
  90. Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Zhenwen Liang, Zhihan Zhang, and Meng Jiang. 2024. Chain-of-layer: Iteratively prompting large language models for taxonomy induction from limited examples. arXiv preprint arXiv:2402.07386. — [arXiv:2402.07386](https://arxiv.org/abs/2402.07386) find
  91. Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang, Qingyi Huang, Saisai Yang, Jing Yuan, Changbao Su, Xiang Li, Aofeng Su, et al. 2023. Tablegpt: Towards unifying tables, nature language and commands into one gpt. arXiv preprint arXiv:2307.08674. — [arXiv:2307.08674](https://arxiv.org/abs/2307.08674) find
  92. Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, and Xipeng Qiu. 2023a. SpeechGPT: Empowering large language models with intrinsic cross-modal conversational abilities. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15757–15773, Singapore. Associ…
  93. Dong Zhang, Xin Zhang, Jun Zhan, Shimin Li, Yaqian Zhou, and Xipeng Qiu. 2024a. Speechgpt-gen: Scaling chain-of-information speech generation. arXiv preprint arXiv:2401.13527. — [arXiv:2401.13527](https://arxiv.org/abs/2401.13527) find
  94. Jiwen Zhang, Jihao Wu, Yihua Teng, Minghui Liao, Nuo Xu, Xiao Xiao, Zhongyu Wei, and Duyu Tang. 2024b. Android in the zoo: Chain-of-action-thought for gui agents. arXiv preprint arXiv:2403.02713. find
  95. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al. 2023b. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792.
  96. Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023c. Siren’s song in the ai ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219.
  97. Zhenduo Zhang, Bowen Zhang, and Guang Liu. 2023d. Coie: Chain-of-instruct editing for multi-attribute face manipulation. arXiv preprint arXiv:2312.07879. — [arXiv:2312.07879](https://arxiv.org/abs/2312.07879) find
  98. Zhuosheng Zhang and Aston Zhang. 2024. You only look at screens: Multimodal chain-of-action agents. arXiv preprint arXiv:2309.11436.
  99. Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. 2023e. Multimodal chain-of-thought reasoning in language models. arXiv preprint arXiv:2302.00923.
  100. Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing. 2023. Verify-and-edit: A knowledge-enhanced chain-of-thought framework. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5823–5840, Toronto, Canada. Associa…
  101. Ge Zheng, Bin Yang, Jiajin Tang, Hong-Yu Zhou, and Sibei Yang. 2023. DDCot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models. In Thirty-seventh Conference on Neural Information Processing Systems. find
  102. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023. Least-to-most prompting enables complex reasoning in large language models. In The Eleventh International Conference on Learning Representation…
  103. Enshen Zhou, Yiran Qin, Zhenfei Yin, Yuzhou Huang, Ruimao Zhang, Lu Sheng, Yu Qiao, and Jing Shao. 2024. Minedreamer: Learning to follow instructions via chain-of-imagination for simulated-world control. arXiv preprint arXiv:2403.12037. — [arXiv:2403.12037](https://arxiv.org/abs/2403.12037) find
  104. Jin Ziqi and Wei Lu. 2023. Tab-CoT: Zero-shot tabular chain of thought. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10259– 10277, Toronto, Canada. Association for Computational Linguistics. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/xia-2025-beyond-chain-thought-survey-chain/