---
title: "AgentInstruct: Toward Generative Teaching with Agentic Flows"
description: "The authors call the task of using strong models to instill a specific skill in another model \"Generative… 40 references, cited by 40 papers here."
authors: ["Arindam Mitra", "Luciano Del Corro", "Guoqing Zheng", "Shweti Mahajan", "Dany Rouhana", "Andres Codas", "Yadong Lu", "Wei-ge Chen", "Olga Vrousgos", "Corby Rosset", "Fillipe Silva", "Hamed Khanpour", "Yash Lara", "Ahmed Awadallah"]
year: 2024
venue: "arXiv:2407.03502"
source: "https://arxiv.org/abs/2407.03502"
doi: "10.48550/arxiv.2407.03502"
arxiv: "2407.03502"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mitra-2024-agentinstruct-toward-generative-teaching-agentic/"
---
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# AgentInstruct: Toward Generative Teaching with Agentic Flows

**Authors:** [Arindam Mitra](https://md.research.elamur.ai/free/en/authors/arindam-mitra/), [Luciano Del Corro](https://md.research.elamur.ai/free/en/authors/luciano-del-corro/), [Guoqing Zheng](https://md.research.elamur.ai/free/en/authors/guoqing-zheng/), [Shweti Mahajan](https://md.research.elamur.ai/free/en/authors/shweti-mahajan/), Dany Rouhana, [Andres Codas](https://md.research.elamur.ai/free/en/authors/andres-codas/), [Yadong Lu](https://md.research.elamur.ai/free/en/authors/yadong-lu/), Wei-ge Chen, Olga Vrousgos, [Corby Rosset](https://md.research.elamur.ai/free/en/authors/corby-rosset/), Fillipe Silva, [Hamed Khanpour](https://md.research.elamur.ai/free/en/authors/hamed-khanpour/), Yash Lara and [Ahmed Awadallah](https://md.research.elamur.ai/free/en/authors/ahmed-hassan-awadallah/) · **Year:** 2024 · **Venue:** arXiv:2407.03502 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2407.03502) · [PDF](https://arxiv.org/pdf/2407.03502) · [DOI 10.48550/arxiv.2407.03502](https://doi.org/10.48550/arxiv.2407.03502) · [arXiv 2407.03502](https://arxiv.org/abs/2407.03502) · [OpenAlex](https://openalex.org/W4400433740)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [40](https://md.research.elamur.ai/free/en/papers/mitra-2024-agentinstruct-toward-generative-teaching-agentic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Post-training language models on synthetic data is appealing, but such data often lacks quality and variety. It can make a student model copy a teacher's style without gaining real ability, and it usually needs heavy manual curation. The authors call the task of using strong models to instill a specific skill in another model "Generative Teaching." For it they propose AgentInstruct, a Microsoft Research framework in which teams of LLM-powered agents, some with tools like search or code interpreters, produce both prompts and answers. The only input is raw text and code. Three pipelines do the work: one transforms the seed content, one generates instructions guided by a taxonomy, and one makes tasks harder through suggester–editor refinement. The authors produced 25 million instruction–response pairs and used them to fine-tune Mistral-7B, yielding Orca-3. Compared with Mistral-7B-Instruct, Orca-3 improves markedly on reasoning, knowledge, math and instruction-following benchmarks. It also beats LLAMA-8B-instruct and GPT-3.5-turbo on several of them. The work points toward scalable, mostly automated data pipelines for teaching general or domain-specific skills.

## Key points
- The dataset contains 25M prompt–response pairs covering 17 skills, guided by a taxonomy of more than 100 subcategories. Skills include reading comprehension, text modification, tool/API use, coding, RAG, creative writing and Web control. No benchmark or task-specific data served as seeds.
- The pipeline has three agentic flows: Content Transformation Flow, Seed Instruction Generation Flow and Instruction Refinement Flow (Suggester-Editor agents).
- Orca-3 outperforms Mistral-7B-Instruct by roughly 40% on AGIEval, 19% on MMLU, 54% on GSM8K, 38% on BBH and 45% on AlpacaEval. It also reduces hallucination on summarization benchmarks by 31.34%.
- On MIRAGE, RAG performance rises by an average of 38.30%. PubMedQA shows the largest gain, about 92.71% relative to Mistral-Instruct.
- The authors list several limitations: building flows still requires manual effort, generation is costly, bias in seed data can be amplified, and synthetic data is hard to validate.

*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

- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 404 works · 163,863 citations

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## Cite

Cite as: Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei-ge Chen, Olga Vrousgos, Corby Rosset, Fillipe Silva, Hamed Khanpour, Yash Lara and Ahmed Awadallah. 2024. AgentInstruct: Toward Generative Teaching with Agentic Flows. arXiv:2407.03502.

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

```bibtex
@misc{mitra2024agentinstruct,
  title = {{AgentInstruct: Toward Generative Teaching with Agentic Flows}},
  author = {Arindam Mitra, Luciano {Del Corro}, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei-ge Chen, Olga Vrousgos, Corby Rosset, Fillipe Silva, Hamed Khanpour, Yash Lara and Ahmed Awadallah},
  year = {2024},
  doi = {10.48550/arxiv.2407.03502},
  eprint = {2407.03502},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2407.03502}
}
```

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## References

40 references, 28 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Qin Cai, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudha… — [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
2. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. [Think you have solved question answering? try arc, the ai2 reasoning challenge](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/). arXiv:1803.05457v1, 2018.
3. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. [Training verifiers to solve math word problems](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/). arXiv preprint arXiv:2110.14168, 2021.
4. CodeParrot. Github-code clean dataset, 2022. [https://huggingface.co/datasets/](https://huggingface.co/datasets/) codeparrot/github-code-clean [Accessed: (06/15/2024)]. [↗](https://huggingface.co/datasets/)
5. Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. [Enhancing chat language models by scaling high-quality instructional conversations](https://md.research.elamur.ai/free/en/papers/ding-2023-enhancing-chat-language-models-scaling/). arXiv preprint arXiv:2305.14233, 2023.
6. Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. [DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Hum…
7. Zhaoye Fei, Yunfan Shao, Linyang Li, Zhiyuan Zeng, Hang Yan, Xipeng Qiu, and Dahua Lin. Query of cc: [Unearthing large scale domain-specific knowledge from public corpora](https://md.research.elamur.ai/free/en/papers/fei-2024-unearthing-large-scale-domain-specific/). arXiv preprint arXiv:2401.14624, 2024.
8. Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song. [The false promise of imitating proprietary llms](https://md.research.elamur.ai/free/en/papers/gudibande-2023-false-promise-imitating-proprietary-llms/), 2023. URL https://arxiv.org/abs/2305.15717. [↗](https://arxiv.org/abs/2305.15717)
9. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. [Measuring mathematical problem solving with the math dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/). arXiv preprint arXiv:2103.03874, 2021.
10. Hamish Ivison, Yizhong Wang, Valentina Pyatkin, Nathan Lambert, Matthew Peters, Pradeep Dasigi, Joel Jang, David Wadden, Noah A. Smith, Iz Beltagy, and Hannaneh Hajishirzi. [Camels in a changing climate: Enhancing lm adaptation with tulu 2](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/), 2023. URL https://arxiv.org/abs/2311.10702. [↗](https://arxiv.org/abs/2311.10702)
11. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,… [find](https://scholar.google.com/scholar?q=Albert+Q%2E+Jiang%2C+Alexandre+Sablayrolles%2C+Arthur+Mensch%2C+Chris+Bamford%2C+Devendra+Singh+Chaplot%2C+Diego+de+las+Casas%2C+Florian+Bressand%2C+Gianna+Lengyel%2C+Guillaume+Lample%2C+Lucile+Saulnier%2C+L%C3%A9lio+Renard+Lav)
12. Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. Rlaif: Scaling reinforcement learning from human feedback with ai feedback, 2023. URL https://arxiv.org/abs/2309.00267. [↗](https://arxiv.org/abs/2309.00267)
13. Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. [Camel: Communicative agents for "mind" exploration of large language model society](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/), 2023. URL https://arxiv.org/abs/2303.17760. [↗](https://arxiv.org/abs/2303.17760)
14. Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Alpacaeval: An automatic evaluator of instruction-following models. [https://github.com/tatsu-lab/alpaca_eval](https://github.com/tatsu-lab/alpaca_eval), 2023. [↗](https://github.com/tatsu-lab/alpaca_eval)
15. Yixin Liu, Alexander R. Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, and Arman Cohan. [Benchmarking generation and evaluation capabilities of large language models for instruction controllable summarization](https://md.research.elamur.ai/free/en/papers/liu-2023-benchmarking-generation-evaluation-capabilities-large/), 2023. URL https://arxiv.org/abs/231… [↗](https://arxiv.org/abs/231…)
16. Lm-sys. Mt-Bench, 2023. URL [https://huggingface.co/spaces/lmsys/mt-bench/tree/](https://huggingface.co/spaces/lmsys/mt-bench/tree/) cf27f9f9da48f72169bce3c3e784d24347d1e833/data/mt_bench/model_answer. [↗](https://huggingface.co/spaces/lmsys/mt-bench/tree/)
17. Daniel van Strien Loubna Ben Allal, Anton Lozhkov. Cosmopedia: how to create large-scale synthetic data for pre-training, 2024. URL [https://huggingface.co/blog/cosmopedia](https://huggingface.co/blog/cosmopedia). [↗](https://huggingface.co/blog/cosmopedia)
18. Arindam Mitra, Luciano Del Corro, Shweti Mahajan, Andres Codas, Clarisse Simoes, Sahaj Agarwal, Xuxi Chen, Anastasia Razdaibiedina, Erik Jones, Kriti Aggarwal, Hamid Palangi, Guoqing Zheng, Corby Rosset, Hamed Khanpour, and Ahmed Awadallah. [Orca 2: Teaching small language models how to reason](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/), 2023.…
19. Arindam Mitra, Hamed Khanpour, Corby Rosset, and Ahmed Awadallah. [Orca-math: Unlocking the potential of slms in grade school math](https://md.research.elamur.ai/free/en/papers/mitra-2024-orca-math-unlocking-potential-slms/). arXiv preprint arXiv:2402.14830, 2024.
20. Subhabrata Mukherjee and Ahmed Awadallah. Xtremedistil: Multi-stage distillation for massive multilingual models, 2020. [find](https://scholar.google.com/scholar?q=Subhabrata+Mukherjee+and+Ahmed+Awadallah%2E+Xtremedistil%3A+Multi%2Dstage+distillation+for+massive+multilingual+models%2C+2020%2E)
21. Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. [Orca: Progressive learning from complex explanation traces of gpt-4](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/). arXiv preprint arXiv:2306.02707, 2023.
22. OpenAI. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/), 2023.
23. Samuel J. Paech. [Eq-bench: An emotional intelligence benchmark for large language models](https://md.research.elamur.ai/free/en/papers/paech-2023-eq-bench-emotional-intelligence-benchmark/), 2024. URL https://arxiv.org/abs/2312.06281. [↗](https://arxiv.org/abs/2312.06281)
24. Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. [Instruction tuning with gpt-4](https://md.research.elamur.ai/free/en/papers/peng-2023-instruction-tuning-gpt-4/), 2023. URL https://arxiv.org/abs/2304.03277. [↗](https://arxiv.org/abs/2304.03277)
25. Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, and Dong Yu. [Infobench: Evaluating instruction following ability in large language models](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/), 2024. URL https://arxiv.org/abs/2401.03601. [↗](https://arxiv.org/abs/2401.03601)
26. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. [Toolllm: Facilitating large language models to master 16000+ real-world apis](https://md.research.elamur.ai/free/en/papers/qin-2023-toolllm-facilitating-large-language-models/), 2023.
27. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. [Gpqa: A graduate-level google-proof q&a benchmark](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/), 2023. URL https://arxiv.org/abs/2311.12022. [↗](https://arxiv.org/abs/2311.12022)
28. Corby Rosset, Ching-An Cheng, Arindam Mitra, Michael Santacroce, Ahmed Awadallah, and Tengyang Xie. [Direct nash optimization: Teaching language models to self-improve with general preferences](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/), 2024. URL https://arxiv.org/abs/2404.03715. [↗](https://arxiv.org/abs/2404.03715)
29. Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. [The curse of recursion: Training on generated data makes models forget](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/), 2024. URL https://arxiv.org/abs/2305.17493. [↗](https://arxiv.org/abs/2305.17493)
30. Mohammed Latif Siddiq, Jiahao Zhang, Lindsay Roney, and Joanna C. S. Santos. Re(gex\|dos)eval: Evaluating generated regular expressions and their proneness to dos attacks. In Proceedings of the 46th International Conference on Software Engineering, NIER Track (ICSE-NIER ’24), 2024. doi: 10.1145/36394… — [doi:10.1145/3639476.3639757](https://doi.org/10.1145/3639476.3639757) [find](https://scholar.google.com/scholar?q=Mohammed+Latif+Siddiq%2C+Jiahao+Zhang%2C+Lindsay+Roney%2C+and+Joanna+C%2E+S%2E+Santos%2E+Re%28gex%5C%7Cdos%29eval%3A+Evaluating+generated+regular+expressions+and+their+proneness+to+dos+attacks%2E+In+Proceedings+of+the+46th+I)
31. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei. [Challenging big-bench tasks and whether chain-of-thought can solve them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/). arXiv preprint arXiv:2210.09261, 2022.
32. Wen wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, and Meliha Yetisgen. Aci-bench: a novel ambient clinical intelligence dataset for benchmarking automatic visit note generation, 2023. [find](https://scholar.google.com/scholar?q=Wen+wai+Yim%2C+Yujuan+Fu%2C+Asma+Ben+Abacha%2C+Neal+Snider%2C+Thomas+Lin%2C+and+Meliha+Yetisgen%2E+Aci%2Dbench%3A+a+novel+ambient+clinical+intelligence+dataset+for+benchmarking+automatic+visit+note+generation%2C+2023%2E)
33. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. [Autogen: Enabling next-gen llm applications via multi-agent conversation](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/), 2023. URL https://arxiv.org/abs/2308.0… [↗](https://arxiv.org/abs/2308.0…)
34. Congying Xia, Chen Xing, Jiangshu Du, Xinyi Yang, Yihao Feng, Ran Xu, Wenpeng Yin, and Caiming Xiong. Fofo: A benchmark to evaluate llms’ format-following capability, 2024. URL https://arxiv.org/abs/2402.18667. [↗](https://arxiv.org/abs/2402.18667)
35. Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang. [Benchmarking retrieval-augmented generation for medicine](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/). arXiv preprint arXiv:2402.13178, 2024.
36. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. Wizardlm: Empowering large language models to follow complex instructions, 2023. [find](https://scholar.google.com/scholar?q=Can+Xu%2C+Qingfeng+Sun%2C+Kai+Zheng%2C+Xiubo+Geng%2C+Pu+Zhao%2C+Jiazhan+Feng%2C+Chongyang+Tao%2C+and+Daxin+Jiang%2E+Wizardlm%3A+Empowering+large+language+models+to+follow+complex+instructions%2C+2023%2E)
37. Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. [Metamath: Bootstrap your own mathematical questions for large language models](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/). arXiv preprint arXiv:2309.12284, 2023.
38. Yifan Zhang, Yifan Luo, Yang Yuan, and Andrew Chi-Chih Yao. Automathtext: Autonomous data selection with language models for mathematical texts. arXiv preprint arXiv:2402.07625, 2024. — [arXiv:2402.07625](https://arxiv.org/abs/2402.07625) [find](https://scholar.google.com/scholar?q=Yifan+Zhang%2C+Yifan+Luo%2C+Yang+Yuan%2C+and+Andrew+Chi%2DChih+Yao%2E+Automathtext%3A+Autonomous+data+selection+with+language+models+for+mathematical+texts%2E+arXiv+preprint+arXiv%3A2402%2E07625%2C+2024%2E+%E2%80%94+%5BarXiv%3A2402%2E07)
39. Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. [Agieval: A human-centric benchmark for evaluating foundation models](https://md.research.elamur.ai/free/en/papers/zhong-2024-agieval-human-centric-benchmark-evaluating/), 2023.
40. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. [Instruction-following evaluation for large language models](https://md.research.elamur.ai/free/en/papers/zhou-2023-instruction-following-evaluation-large-language/), 2023. URL https://arxiv.org/abs/2311.07911. [↗](https://arxiv.org/abs/2311.07911)

*Updated 3 November 2026*