ChatDev: Communicative Agents for Software Development
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Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, Juyuan Xu, Dahai Li, Zhiyuan Liu, Maosong Sun. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
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In ChatDev, a virtual CEO meta-agent distributes subtasks to departmental agents and integrates their outputs into a unified strategic response.
“In ChatDev [153], for example, a virtual CEO meta-agent distributes subtasks to departmental agents and integrates their outputs into a unified strategic response.”
From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [153]
Section 3.1 (page unavailable) describes specialized agents collaborating on sequential software-development subtasks, with each subtask handled by an instructor–assistant pair. It does not describe a virtual CEO distributing work to departmental agents or integrating their outputs into a unified strategic response. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMultiple LLM agents working in parallel can solve tasks that are too large for any single agent to finish within its iteration budget.
“Multiple LLM agents working in parallel can solve tasks that are too large for any single agent to finish within its iteration budget (Dong et al., 2024; Qian et al., 2024; Hong et al., 2024; Geng and Neubig, 2026; Qu et al., 2026).”
From Multi-agent Collaboration with State Management (2026) · cites this paper as Qian et al., 2024
Section 3.1 (Chat Chain; page unavailable) describes multiple agents collaborating to complete software-development subtasks. But it specifies sequential phases and subtasks, not agents working in parallel, and does not say the tasks exceed a single agent’s iteration budget. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMost multi-agent coding systems rely on natural-language soft constraints rather than mathematically explicit safety boundaries.
“yet most of them still rely on natural-language soft constraints rather than mathematically explicit safety boundaries.”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
The Introduction describes ChatDev as coordinating agents through language-based communication, and the Evaluation section says MetaGPT agents follow static instructions. These examples support the natural-language aspect, but the paper does not establish that most multi-agent coding systems use soft constraints or compare them with mathematically explicit safety boundaries. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSoft coordination in multi-agent frameworks can be brittle under adversarial prompts, hallucinated critiques, or multi-turn confirmation bias.
“Such soft coordination can be brittle under adversarial prompts, hallucinated critiques, or multi-turn confirmation bias.”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
The Introduction and §3.2, Communicative Dehallucination, say that language-based cooperation among agents can produce coding hallucinations that undermine software reliability. They do not identify adversarial prompts, hallucinated critiques, or multi-turn confirmation bias as causes of brittle coordination. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSuch frameworks are particularly well-suited for multi-agent execution scenarios with well-defined agent execution sequences, as exemplified by programming tasks and conversational games, as they can significantly simplify complex agent interaction processes.
“Such frameworks are particularly well-suited for multi-agent execution scenarios with well-defined agent execution sequences, as exemplified by programming tasks [50, 85] and conversational games [111], as they can significantly simplify complex agent interaction processes.”
From AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society (2025) · cites this paper as [85]
The formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software or conducting scientific inquiry.
“This formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software (Qian et al., 2024; Wang et al., 2025l) or conducting scientific inquiry (Weng et al., 2025).”
From Memory in the Age of AI Agents (2025) · cites this paper as Qian et al., 2024
The proposed formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software or conducting scientific inquiry.
“This formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software (Qian et al., 2024; Wang et al., 2025l) or conducting scientific inquiry (Weng et al., 2025).”
From Memory in the Age of AI Agents (2025) · cites this paper as Qian et al., 2024
Building a software system from scratch requires agents to translate high-level requirements into executable plans, coordinate interdependent tasks, design and integrate components, and continuously test and debug the evolving system.
“Building a software system from scratch requires agents not only to generate code snippets, but also to translate high-level requirements into executable plans, coordinate interdependent tasks, design and integrate components, and continuously test and debug the evolving system [8, 32, 36].”
From Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement (2026) · cites this paper as [32]
Existing multi-agent coding systems predominantly use sequential turn-taking: design → implement → review, each agent waiting for the previous.
“Multi-agent coding systems [Qian et al., 2024, Hong et al., 2024, Nguyen et al., 2024, Huang et al., 2024, Ishibashi and Nishimura, 2024, Pan et al., 2026, Islam et al., 2025, Peng et al., 2026, Zhu et al., 2025, Wu et al., 2023, Wang et al., 2025, Park et al., 2023, Chen et al., 2023, Shinn et al., 2023, Ashrafi et al., 2025, Grötschla et al., 2025, Shafin et al., 2025] predominantly use sequ”
From AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace (2026) · cites this paper as Qian et al., 2024
Multi-agent coding systems improve decomposition and repair by assigning roles to planners, developers, testers, and reviewers.
“Multi-agent coding systems improve decomposition and repair by assigning roles to planners, developers, testers, and reviewers [11,12,13]”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
When subtasks exhibit strict contextual dependencies, hierarchical workflows offer a structured alternative.
“When subtasks exhibit strict contextual dependencies, hierarchical (Zhang et al., 2024c; Qian et al., 2024) workflows offer a structured alternative.”
From A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems (2025) · cites this paper as Qian et al., 2024
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Research works printed but not linked: 13
- Thorsten Brants, Ashok C Popat, Peng Xu, Franz J Och, and Jeffrey Dean. 2007. Large Language Models in Machine Translation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 858- 867.
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Ad- vances in Neural Information Processing Systems (NeurIPS), volume 33, pages 1877-1901.
- Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. 2023b. AgentVerse: Facilitating Multi-agent Collaboration and Explor- ing Emergent Behaviors in Agents. In International Conference on Learning Representations (ICLR).
- Robert France and Bernhard Rumpe. 2007. Model- driven Development of Complex Software: A Re- search Roadmap. In Future of Software Engineering (FOSE), pages 37-54.
- Peter Freeman, Donald J. Bagert, Hossein Saiedian, Mary Shaw, Robert Dupuis, and J. Barrie Thompson. 2001. Software Engineering Body of Knowledge (SWEBOK). In Proceedings of the International Conference on Software Engineering (ICSE), pages 693-696.
- Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2023. MetaGPT: Meta Pro- gramming for A Multi-Agent Collaborative Frame- work. In International Conference on Learning Rep- resentations (ICLR).
- Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023a. CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS).
- Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023. CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthe- sis. In The International Conference on Learning Representations (ICLR).
- Florian Pudlitz, Florian Brokhausen, and Andreas Vo- gelsang. 2019. Extraction of System States from Natural Language Requirements. In IEEE Interna- tional Requirements Engineering Conference (RE), pages 211-222. · DOI
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Advances in Neural Information Pro- cessing Systems (NeurIPS), volume 30.
- Chengcheng Wan, Shicheng Liu, Sophie Xie, Yifan Liu, Henry Hoffmann, Michael Maire, and Shan Lu. 2022. Automated Testing of Software that Uses Ma- chine Learning APIs. In IEEE/ACM International Conference on Software Engineering (ICSE), pages 212-224. · DOI
- Song Wang, Nishtha Shrestha, Abarna Kucheri Subbu- raman, Junjie Wang, Moshi Wei, and Nachiappan Nagappan. 2021. Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We? In IEEE/ACM International Conference on Software Engineering (ICSE), pages 1548-1560. · DOI
- Jonas Winkler, Jannis Grönberg, and Andreas Vogel- sang. 2020. Predicting How to Test Requirements: An Automated Approach. In Software Engineering, volume P-300 of LNI, pages 141-142. · DOI
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Hou, S., Shen, Z., Liang, J., Jiao, H., Zhao, A., Qing, Y., Peng, D., Gui, Z., Guan, X., Xiang, L., et al. (2025) · Geo-spatial Information Science · doi:10.1080/10095020.2025.2535523 · Cited by 17 · see citing works
A Survey of Task Planning with Large Language Models
Zhai, W., Liao, J., Chen, Z., Su, B., & Zhao, X. (2025) · Intelligent Computing · doi:10.34133/icomputing.0124 · Cited by 16 · see citing works
Fixing Large Language Models' Specification Misunderstanding for Better Code Generation
Tian, Z., Chen, J.-J., & Zhang, X.-Y. (2025) · International Conference on Software Engineering · doi:10.1109/icse55347.2025.00108
War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
Hua, W., Fan, L., Li, L., Mei, K., Ji, J., Ge, Y., Hemphill, L., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2311.17227 · Cited by 15 · see citing works
Ronanki, K. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728717 · Cited by 12 · see citing works
Woensel, W. V., & Motie, S. (2024) · Enterprise Information Systems · doi:10.1080/17517575.2024.2417404 · Cited by 13 · see citing works
Project Sid: Many-agent simulations toward AI civilization
AL, A., Ahn, A., Becker, N., Carroll, S., Christie, N., Cortes, M., Demirci, A., Du, M., Li, F., Luo, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.00114 · Cited by 12 · see citing works
“Multi-agent frameworks have been deployed in software development (43; 27), cooperative robotic control (60), scientific experiments (12; 47), and debates (3).”
BackgroundTakata, R., Masumori, A., & Ikegami, T. (2024) · Entropy · doi:10.3390/e26121092 · Cited by 11 · see citing works
MAO: A Framework for Process Model Generation With Multi-Agent Orchestration
Lin, L., Jin, Y., Zhou, Y., Chen, W., & Qian, C. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3611816 · Cited by 11 · see citing works
Self-citationGuan, T. (2025) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1145/3795154.3795362 · Cited by 10 · see citing works
Liu, Y., Wen, Z., Weng, L., Woodman, O., Yang, Y., & Chen, W. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3410523 · Cited by 10 · see citing works
Self-citationGenerative Agents in Agent-Based Modeling: Overview, Validation, and Emerging Challenges
Adornetto, C., Mora, A., Hu, K., Garcia, L. I., Atchade-Adelomou, P., Greco, G., Alonso, L., & Larson, K. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3566362 · Cited by 10 · see citing works
Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs
Dhulshette, N., Shah, S., & Kulkarni, V. (2025) · International Workshop on Large Language Models for Code (LLM4Code) · doi:10.1109/llm4code66737.2025.00023 · Cited by 12 · see citing works
DataLab: A Unified Platform for LLM-Powered Business Intelligence
Weng, L., Tang, Y., Feng, Y., Chang, Z., Chen, R., Feng, H., Hou, C., Huang, D., Li, Y., Rao, H., et al. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00326 · Cited by 15 · see citing works
Self-citationCan AI with High Reasoning Ability Replicate Human-like Decision Making in Economic Experiments?
Kitadai, A., Lugo, S. D. R., Tsurusaki, Y., Fukasawa, Y., & Nishino, N. (2025) · Group Decision and Negotiation · doi:10.1007/s10726-025-09946-9 · Cited by 10 · see citing works
Generative AI Agents for Knowledge Work Augmentation in Finance
Ganesh, S., Ardon, L., Borrajo, D., Garg, D., Madhushani, U., Narayanan, A. L., Canonaco, G., & Veloso, M. (2024) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-022823-033813 · Cited by 10 · see citing works
Cinema Multiverse Lounge: Enhancing Film Appreciation via Multi-Agent Conversations
Ryu, J., Kim, K., Heo, D., Song, H., Oh, C., & Suh, B. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713641 · Cited by 9 · see citing works
AgentsBench: A Multi-Agent LLM Simulation Framework for Legal Judgment Prediction
Jiang, C., & Yang, X. (2025) · Syst. · doi:10.3390/systems13080641 · Cited by 17 · see citing works
An LLM-based multi-agent system to assist early-stage product design and evaluation
Chen, P., Cai, Y., Zhou, Z., Yao, J., Li, J., You, W., & Sun, L. (2026) · Journal of Engineering Design · doi:10.1080/09544828.2026.2616583 · Cited by 9 · see citing works
Self-citationTriangle: Empowering Incident Triage with Multi-Agent
Yu, Z., Fang, A., Ma, M., Walia, J. S., Zhang, C., Chi, S., Li, Z., Chintalapati, M., Zhang, X., Wang, R., et al. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00062 · Cited by 10 · see citing works
Nonomura, R., & Mori, H. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1582287 · Cited by 9 · see citing works
DAWN: Designing Distributed Agents in a Worldwide Network
Aminiranjbar, Z., Tang, J., Wang, Q., Pant, S., & Viswanathan, M. (2025) · IEEE Access · doi:10.1109/access.2025.3588425 · Cited by 8 · see citing works
Qi, M.-F., Zhu, T.-Q., Zhang, L.-F., Li, N.-R., Tan, Y.-A., & Zhou, W.-L. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3659486 · Cited by 5 · see citing works
Understanding Agentic AI: Algorithms and Infrastructure
Ma, W.-L., Guo, Y.-J., Han, Q.-L., Zhou, W., Zhu, X.-G., Xiong, J., Wen, S., & Xiang, Y. (2026) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2026.125993 · Cited by 11 · see citing works
DAWN: Designing Distributed Agents in a Worldwide Network
Aminiranjbar, Z., Tang, J., Wang, Q., Pant, S., & Viswanathan, M. (2024) · arXiv · doi:10.48550/arxiv.2410.22339 · Cited by 8 · see citing works
KARMA: Augmenting Embodied AI Agents with Long-and-Short Term Memory Systems
Wang, Z., Yu, B., Zhao, J., Sun, W., Hou, S., Liang, S., Hu, X., Han, Y., & Gan, Y. (2025) · 2025 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra55743.2025.11128047 · Cited by 11 · see citing works
Scaling Large Language Model-based Multi-Agent Collaboration
Qian, C., Xie, Z., Wang, Y., Liu, W., Zhu, K., Xia, H., Dang, Y., Du, Z., Chen, W., Yang, C., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.07155 · Cited by 8 · see citing works
“To address this, we adopt a heuristic mechanism (Qian et al., 2024b) to manage context visibility using short-term and long-term memory.”
InfluentialSelf-citationDong, Y., Zhu, X., Pan, Z., Zhu, L., & Yang, Y. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.964 · Cited by 13 · see citing works
An Interview System Based on Large Language Models and Multi-Agent Interactions
Quan, X., Zhang, Y., Xiang, X., & Wang, Y. (2024) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd64080.2024.10702317 · Cited by 7 · see citing works
Cao, H., Ma, R., Zhai, Y., & Shen, J. (2024) · Applied Computing and Intelligence · doi:10.3934/aci.2024019 · Cited by 7 · see citing works
Chen, Z., & Jiang, L. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00068 · Cited by 7 · see citing works
Deploying Foundation Model Powered Agent Services: A Survey
Xu, W., Chen, J., Zheng, P., Yi, X., Tian, T., Zhu, W., Wan, Q., Wang, H., Fan, Y., Su, Q., et al. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3580745 · Cited by 16 · see citing works
Multi-Agent LLMs for Automating Sustainable Operational Decision-Making
Pajak, E., Bahamdan, A., Hellgardt, K., & R�o-Chanona, A. (2025) · Systems and Control Transactions · doi:10.69997/sct.156776 · Cited by 7 · see citing works
Collaborative Tree Search for Enhancing Embodied Multi-Agent Collaboration
Zu, L., Lin, L., Fu, S., Zhao, N., & Zhou, P. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02748 · Cited by 7 · see citing works
Zhao, Z., Yue, X., Xie, J., Fang, C., Shao, Z., & Guo, S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3533476 · Cited by 8 · see citing works
Zeng, Y., Brown, C., Byari, M., Raymond, J., Schmitt, T. M., & Rounsevell, M. (2025) · Geoscientific model development · doi:10.5194/gmd-18-4983-2025 · Cited by 7 · see citing works
A multi-agent-driven robotic AI chemist enabling autonomous chemical research on demandPreprint
Song, T., Luo, M., Chen, L., Huang, Y. H., Zhu, Q. J., Liu, D., Zhang, B., Zou, G., Zhang, F., Shang, W., et al. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-w953h-v2 · Cited by 7 · see citing works
Fang, J., Peng, Y., Zhang, X., Wang, Y., Yi, X., Zhang, G., Xu, Y., Wu, B., Liu, S., Li, Z., et al. (2025) · arXiv · doi:10.48550/arxiv.2508.07407 · Cited by 6 · see citing works
A Self-Evolving Framework for Multi-Agent Medical Consultation Based on Large Language Models
Chen, K., Qi, J., Huo, J., Tian, P., Meng, F., Yang, X., & Gao, Y. (2025) · ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49660.2025.10889517 · Cited by 6 · see citing works
Agentic AI for Medicare Annual Wellness Visits: Improving Preventive Care
Viradia, V., Mishra, V. K., & -, S. P. (2025) · International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart66413.2025.11046123 · Cited by 6 · see citing works
Yang, Y., Wang, P., Liu, X., Luo, W., & Yang, L. (2025) · Water Resources Management · doi:10.1007/s11269-025-04312-5 · Cited by 6 · see citing works
A conversational multi-agent AI system for automated plant phenotyping
Chen, F., Stogiannidis, I., Wood, A., Bueno, D., Williams, D., Macfarlane, F., Grieve, B., Wells, D. M., Atkinson, J. A., Hawkesford, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-026-71090-y · Cited by 13 · see citing works
Envisioning Intelligent Requirements Engineering via Knowledge-Guided Multi-Agent Collaboration
Huang, J.-P., Jin, D.-M., Sun, W.-S., Liu, Y., & Jin, Z. (2025) · International Conference on Automated Software Engineering · doi:10.1109/ase63991.2025.00334 · Cited by 6 · see citing works
- Authors
- Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., & Sun, M.
- Affiliations
- The University of Sydney (AU)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 15174-15186
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402684050
- Semantic Scholar
- 9474b463654fd92a62c103b21f95aed94b087907
- Cited by
- 490
- References
- 0
- Citations by year
- 2026: 313 · 2025: 151 · 2024: 26
- FWCI
- 157.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Tencent
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, software development, code generation, code review, developer communication, chatbots
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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