Fetching the paper…
Reading the bibliography…
The rapid development of large language models (LLMs), such as ChatGPT, has revolutionized the efficiency of creating programming tutorials.
C. Kojouharov, A. Solodovnik, and G. Naumovich, “Jtutor: an eclipse plug-in suite for creation and replay of code-based tutorials,” in Proc. ETX , 2004, pp. 27–31
2004
Earlier work this paper cites.
C. Oezbek and L. Prechelt, “Jtourbus: Simplifying program understanding by documentation that provides tours through the source code,” in Proc. IEEE ICSM , 2007, pp. 64–73
2007
Earlier work this paper cites.
S. M. Nasehi, J. Sillito, F. Maurer, and C. Burns, “What makes a good code example?: A study of programming q&a in stackoverflow,” in Proc. IEEE ICSM , 2012, pp. 25–34
2012
Earlier work this paper cites.
S. Ginosar, L. F. D. Pombo, M. Agrawala, and B. Hartmann, “Authoring multi-stage code examples with editable code histories,” in Proc. ACM UIST , 2013, pp. 485–494
2013
Earlier work this paper cites.
R. Tiarks and W. Maalej, “How does a typical tutorial for mobile development look like?” in Proc. ACM MSR , 2014, pp. 272–281
2014
Earlier work this paper cites.
A. S. Kim and A. J. Ko, “A pedagogical analysis of online coding tutorials,” in Proc. ACM SIGCSE , 2017, pp. 321–326
2017
Earlier work this paper cites.
H. Jiang, J. Zhang, Z. Ren, and T. Zhang, “An unsupervised approach for discovering relevant tutorial fragments for apis,” in Proc. IEEE/ACM ICSE , 2017, pp. 38–48
2017
Earlier work this paper cites.
A. Mysore and P. J. Guo, “Torta: Generating mixed-media GUI and command-line app tutorials using operating-system-wide activity tracing,” in Proc. ACM UIST , 2017, pp. 703–714
2017
Earlier work this paper cites.
S. Oney, C. Brooks, and P. Resnick, “Creating guided code explanations with chat.codes,” Proc. ACM Hum. Comput. Interact. , vol. 2, no. CSCW, pp. 131:1–131:20, 2018
2018
Earlier work this paper cites.
A. Head, E. L. Glassman, B. Hartmann, and M. A. Hearst, “Interactive extraction of examples from existing code,” in Proc. ACM CHI , 2018, p. 85
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. Hamouda, S. H. Edwards, H. G. Elmongui, J. V. Ernst, and C. A. Shaffer, “Recurtutor: An interactive tutorial for learning recursion,” ACM Trans. Comput. Educ. , vol. 19, no. 1, pp. 1:1–1:25, 2019
2019
Earlier work this paper cites.
L. Bao, Z. Xing, X. Xia, and D. Lo, “Vt-revolution: Interactive programming video tutorial authoring and watching system,” IEEE Trans. Software Eng. , vol. 45, no. 8, pp. 823–838, 2019
2019
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proc. NeurIPS , 2020
2020
Earlier work this paper cites.
A. Head, J. Jiang, J. Smith, M. A. Hearst, and B. Hartmann, “Composing flexibly-organized step-by-step tutorials from linked source code, snippets, and outputs,” in Proc. ACM CHI , 2020, pp. 1–12
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Y. Wang, D. Wang, J. Drozdal, M. J. Muller, S. Park, J. D. Weisz, X. Liu, L. Wu, and C. Dugan, “Documentation matters: Human-centered AI system to assist data science code documentation in computational notebooks,” ACM Trans. Comput. Hum. Interact. , vol. 29, no. 2, pp. 17:1–17:33, 2022
2022
Earlier work this paper cites.
K. I. Gero, V. Liu, and L. Chilton, “Sparks: Inspiration for science writing using language models,” in Proc. ACM DIS , 2022, pp. 1002–1019
2022
Earlier work this paper cites.
F. F. Xu, U. Alon, G. Neubig, and V. J. Hellendoorn, “A systematic evaluation of large language models of code,” in MAPS@PLDI , 2022, pp. 1–10
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in Proc. NeurIPS , 2022
2022
Earlier work this paper cites.
A. Yuan, A. Coenen, E. Reif, and D. Ippolito, “Wordcraft: story writing with large language models,” in Proc. ACM IUI , New York, NY, USA, 2022, pp. 841–852
2022
Earlier work this paper cites.
E. L. Ouh, B. K. S. Gan, and D. Lo, “ITSS: interactive web-based authoring and playback integrated environment for programming tutorials,” in Proc. ICSE (SEET) , 2022, pp. 158–164
2022
Earlier work this paper cites.
J. Y. Khan and G. Uddin, “Automatic code documentation generation using gpt-3,” in Proc. ACM ASE , 2022, pp. 1–6
2022
Earlier work this paper cites.
S. MacNeil, A. Tran, J. Leinonen, P. Denny, J. Kim, A. Hellas, S. Bernstein, and S. Sarsa, “Automatically generating CS learning materials with large language models,” CoRR , 2022
2022
Earlier work this paper cites.
S. MacNeil, A. Tran, D. Mogil, S. Bernstein, E. Ross, and Z. Huang, “Generating diverse code explanations using the gpt-3 large language model,” in Proc. ACM ICER , New York, NY, USA, 2022, pp. 37–39
2022
Cited alongside, same era.
S. Sarsa, P. Denny, A. Hellas, and J. Leinonen, “Automatic generation of programming exercises and code explanations using large language models,” in Proc. ACM ICER , New York, NY, USA, 2022, pp. 27–43
2022
Cited alongside, same era.
T. Wu, E. Jiang, A. Donsbach, J. Gray, A. Molina, M. Terry, and C. J. Cai, “Promptchainer: Chaining large language model prompts through visual programming,” in Proc. ACM CHI , 2022, pp. 1–10
2022
Cited alongside, same era.
T. Wu, M. Terry, and C. J. Cai, “Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts,” in Proc. ACM CHI , 2022, pp. 1–22
2022
Cited alongside, same era.
2023
Closest in time.
Y. Feng, X. Wang, K. K. Wong, S. Wang, Y. Lu, M. Zhu, B. Wang, and W. Chen, “Promptmagician: Interactive prompt engineering for text-to-image creation,” IEEE Trans. Vis. Comput. Graph. , 2023
2023
Closest in time.
P. Jiang, J. Rayan, S. P. Dow, and H. Xia, “Graphologue: Exploring large language model responses with interactive diagrams,” in Proc. ACM UIST , 2023, pp. 3:1–3:20
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Jiang, K. Olson, E. Toh, A. Molina, A. Donsbach, M. Terry, and C. J. Cai, “Promptmaker: Prompt-based prototyping with large language models,” in Proc. ACM CHI , 2022, pp. 35:1–35:8
2022
Cited alongside, same era.
S. M. Goodman, E. Buehler, P. Clary, A. Coenen, A. Donsbach, T. N. Horne, M. Lahav, R. MacDonald, R. B. Michaels, A. Narayanan et al. , “Lampost: Design and evaluation of an ai-assisted email writing prototype for adults with dyslexia,” in Proc. ACM SIGACCESS , 2022, pp. 1–18
2022
Cited alongside, same era.
J. J. Y. Chung, W. Kim, K. M. Yoo, H. Lee, E. Adar, and M. Chang, “Talebrush: Sketching stories with generative pretrained language models,” in Proc. ACM CHI , 2022, pp. 1–19
2022
Cited alongside, same era.
Y. Feng, J. Chen, K. Huang, J. K. Wong, H. Ye, W. Zhang, R. Zhu, X. Luo, and W. Chen, “iPoet: interactive painting poetry creation with visual multimodal analysis,” J. Vis. , vol. 25, no. 3, pp. 671–685, Jun 2022
2022
Cited alongside, same era.
M. Yu, Y. Wang, X. Yu, G. Shan, and Z. Jin, “Pubexplorer: An interactive analytical system for visualizing publication data,” Vis. Informatics , vol. 7, no. 3, pp. 65–74, 2023
2022
Cited alongside, same era.
N. Sultanum and A. Srinivasan, “Datatales: Investigating the use of large language models for authoring data-driven articles,” in Proc. IEEE VIS , 2023, pp. 231–235
2023
Cited alongside, same era.
S. Biswas, “Chatgpt and the future of medical writing,” p. e223312, 2023
2023
Cited alongside, same era.
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Comput. Surv. , vol. 55, no. 12, pp. 248:1–248:38, 2023
2023
Cited alongside, same era.
2023
Closest in time.
S. Suh, B. Min, S. Palani, and H. Xia, “Sensecape: Enabling multilevel exploration and sensemaking with large language models,” in Proc. ACM UIST , 2023, pp. 1:1–1:18
2023
Closest in time.
S. Petridis, N. Diakopoulos, K. Crowston, M. Hansen, K. Henderson, S. Jastrzebski, J. V. Nickerson, and L. B. Chilton, “Anglekindling: Supporting journalistic angle ideation with large language models,” in Proc. ACM CHI , 2023, pp. 1–16
2023
Closest in time.
T. S. Kim, Y. Lee, M. Chang, and J. Kim, “Cells, generators, and lenses: Design framework for object-oriented interaction with large language models,” in Proc. ACM UIST , New York, NY, USA, 2023
2023
Closest in time.
OpenAI, “Introducing ChatGPT,” https://openai.com/blog/chatgpt, [Online; accessed 2023-04-30]
2023
Closest in time.
OpenAI, “Introducing text and code embeddings,” https://openai.com/blog/introducing-text-and-code-embeddings, [Online; accessed 2023-10-01]
2023
Closest in time.
Q. Li, Z. Yu, H. Xu, and B. Guo, “Human-machine interactive streaming anomaly detection by online self-adaptive forest,” Frontiers Comput. Sci. , vol. 17, no. 2, p. 172317, 2023
2023
Closest in time.
Y. Feng, X. Wang, B. Pan, K. K. Wong, Y. Ren, S. Liu, Z. Yan, Y. Ma, H. Qu, and W. Chen, “XNLI: Explaining and diagnosing nli-based visual data analysis,” IEEE Trans. Vis. Comput. Graph. , pp. 1–14, 2023
2023
Closest in time.
X. Wang, Z. Wu, W. Huang, Y. Wei, Z. Huang, M. Xu, and W. Chen, “VIS+AI: integrating visualization with artificial intelligence for efficient data analysis,” Frontiers Comput. Sci. , vol. 17, no. 6, p. 176709, 2023
2023
Closest in time.
A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, and J. M. Zhang, “Large language models for software engineering: Survey and open problems,” 2023
2023
Closest in time.
2023
Closest in time.
A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, and D. S. W. Ting, “Large language models in medicine,” Nature medicine , vol. 29, no. 8, pp. 1930–1940, 2023
2023
Closest in time.
Y. Jin, F. Zhu, J. Li, and L. Ma, “Tcmfvis: A visual analytics system toward bridging together traditional chinese medicine and modern medicine,” Vis. Informatics , vol. 7, no. 1, pp. 41–55, 2023
2023
Closest in time.
2023
Closest in time.
E. Kasneci, K. Seßler, S. Küchemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. Günnemann, E. Hüllermeier et al. , “Chatgpt for good? on opportunities and challenges of large language models for education,” Learning and individual differences , vol. 103, p. 102274, 2023
2023
Closest in time.
B. Wang, Y. Li, Z. Lv, H. Xia, Y. Xu, and R. Sodhi, “Lave: Llm-powered agent assistance and language augmentation for video editing,” in Proc. ACM IUI , 2024, pp. 699–714
2024
Closest in time.
I. Arawjo, C. Swoopes, P. Vaithilingam, M. Wattenberg, and E. L. Glassman, “Chainforge: A visual toolkit for prompt engineering and LLM hypothesis testing,” in Proc. ACM CHI , 2024, pp. 304:1–304:18
2024
Closest in time.
D. Masson, S. Malacria, G. Casiez, and D. Vogel, “Directgpt: A direct manipulation interface to interact with large language models,” in Proc. ACM CHI , 2024, pp. 1–16
2024
Closest in time.
M. Geng, S. Wang, D. Dong, H. Wang, G. Li, Z. Jin, X. Mao, and X. Liao, “Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning,” in Proc. ICSE , 2024, pp. 39:1–39:13
2024
Closest in time.