Built on

D. Dalal and B. V. Galbraith, “Evaluating sequence-to-sequence learning models for if-then program synthesis,” CoRR , vol. abs/2002.03485, 2020. [Online]. Available: https://arxiv.org/abs/2002.03485
Original
2002
Earlier work this paper cites.
J. Dean and S. Ghemawat, “Mapreduce: Simplified data processing on large clusters,” Commun. ACM , vol. 51, no. 1, p. 107–113, jan 2008
2008
Earlier work this paper cites.
E. Luger and A. Sellen, “”Like having a really bad PA”: The gulf between user expectation and experience of conversational agents,” in Proceedings of CHI 2016 , 2016
2016
Earlier work this paper cites.
P. J. Rani, J. Bakthakumar, B. P. Kumaar, U. P. Kumaar, and S. Kumar, “Voice controlled home automation system using natural language processing (nlp) and internet of things (iot),” in 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM) , 2017, pp. 368–373
2017
Earlier work this paper cites.
R. D. Manu, S. Kumar, S. Snehashish, and K. Rekha, “Smart home automation using iot and deep learning,” International Research Journal of Engineering and Technology , vol. 6, no. 4, pp. 1–4, 2019
2019
Earlier work this paper cites.
V. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih, “Dense passage retrieval for open-domain question answering,” arXiv preprint arXiv:2004.04906 , 2020
Original
2020
Earlier work this paper cites.
W. Wang, F. Wei, L. Dong, H. Bao, N. Yang, and M. Zhou, “Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers,” CoRR , vol. abs/2002.10957, 2020
Original
2020
Earlier work this paper cites.
C.-W. C. Ki, E. Cho, and J.-E. Lee, “Can an intelligent personal assistant (ipa) be your friend? para-friendship development mechanism between ipas and their users,” Computers in Human Behavior , vol. 111, p. 106412, 2020
2020
Earlier work this paper cites.
G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V. Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, and L. Schmidt, “Openclip,” Jul. 2021, if you use this software, please cite it as below. [Online]. Available: https://doi.org/10.5281/zenodo.5143773
2021
Earlier work this paper cites.
H. Yu, J. Hua, and C. Julien, “Dataset: Analysis of IFTTT recipes to study how humans use internet-of-things (iot) devices,” CoRR , vol. abs/2110.00068, 2021
Original
2021
Earlier work this paper cites.

Similar

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629 , 2022
Original
2022
Cited alongside, same era.
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” 2022. [Online]. Available: https://arxiv.org/abs/2212.04356
Original
2022
Cited alongside, same era.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Cited alongside, same era.

Then

“Vosk speech recognition toolbox,” https://github.com/alphacep/vosk-api
2023
Closest in time.
“Langchain openapi toolkit,” https://python.langchain.com/docs/integrations/toolkits/openapi
2023
Closest in time.
“Smartthings api,” https://developer.smartthings.com/docs/api/public
2023
Closest in time.
Y. Xu, H. Su, C. Xing, B. Mi, Q. Liu, W. Shi, B. Hui, F. Zhou, Y. Liu, T. Xie, Z. Cheng, S. Zhao, L. Kong, B. Wang, C. Xiong, and T. Yu, “Lemur: Harmonizing natural language and code for language agents,” 2023
2023
Closest in time.
“Smartthings smartapp documentation,” https://developer.smartthings.com/docs/connected-services/create-a-smartapp
2023
Closest in time.
S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” arXiv preprint arXiv:2305.10601 , 2023

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Reading the bibliography…

2023

SAGE: Smart home Agent with Grounded Execution

Rivkin, Dmitriy, Hogan, Francois, Feriani, Amal et al.

Understand

The common sense reasoning abilities and vast general knowledge of Large Language Models (LLMs) make them a natural fit for interpreting user requests in a Smart Home assistant context.

LLMs, however, lack specific knowledge about the user and their home limit their potential impact.
SAGE (Smart Home Agent with Grounded Execution), overcomes these and other limitations by using a scheme in which a user request triggers an LLM-controlled sequence of discrete actions.
These actions can be used to retrieve information, interact with the user, or manipulate device states.

Built on

D. Dalal and B. V. Galbraith, “Evaluating sequence-to-sequence learning models for if-then program synthesis,” CoRR , vol. abs/2002.03485, 2020. [Online]. Available: https://arxiv.org/abs/2002.03485
Original
2002
Earlier work this paper cites.
J. Dean and S. Ghemawat, “Mapreduce: Simplified data processing on large clusters,” Commun. ACM , vol. 51, no. 1, p. 107–113, jan 2008
2008
Earlier work this paper cites.
E. Luger and A. Sellen, “”Like having a really bad PA”: The gulf between user expectation and experience of conversational agents,” in Proceedings of CHI 2016 , 2016
2016
Earlier work this paper cites.
P. J. Rani, J. Bakthakumar, B. P. Kumaar, U. P. Kumaar, and S. Kumar, “Voice controlled home automation system using natural language processing (nlp) and internet of things (iot),” in 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM) , 2017, pp. 368–373
2017
Earlier work this paper cites.
R. D. Manu, S. Kumar, S. Snehashish, and K. Rekha, “Smart home automation using iot and deep learning,” International Research Journal of Engineering and Technology , vol. 6, no. 4, pp. 1–4, 2019
2019
Earlier work this paper cites.
V. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih, “Dense passage retrieval for open-domain question answering,” arXiv preprint arXiv:2004.04906 , 2020
Original
2020
Earlier work this paper cites.
W. Wang, F. Wei, L. Dong, H. Bao, N. Yang, and M. Zhou, “Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers,” CoRR , vol. abs/2002.10957, 2020
Original
2020
Earlier work this paper cites.
C.-W. C. Ki, E. Cho, and J.-E. Lee, “Can an intelligent personal assistant (ipa) be your friend? para-friendship development mechanism between ipas and their users,” Computers in Human Behavior , vol. 111, p. 106412, 2020
2020
Earlier work this paper cites.
G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V. Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, and L. Schmidt, “Openclip,” Jul. 2021, if you use this software, please cite it as below. [Online]. Available: https://doi.org/10.5281/zenodo.5143773
2021
Earlier work this paper cites.
H. Yu, J. Hua, and C. Julien, “Dataset: Analysis of IFTTT recipes to study how humans use internet-of-things (iot) devices,” CoRR , vol. abs/2110.00068, 2021
Original
2021
Earlier work this paper cites.

Similar

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629 , 2022
Original
2022
Cited alongside, same era.
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” 2022. [Online]. Available: https://arxiv.org/abs/2212.04356
Original
2022
Cited alongside, same era.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Cited alongside, same era.

Then

“Vosk speech recognition toolbox,” https://github.com/alphacep/vosk-api
2023
Closest in time.
“Langchain openapi toolkit,” https://python.langchain.com/docs/integrations/toolkits/openapi
2023
Closest in time.
“Smartthings api,” https://developer.smartthings.com/docs/api/public
2023
Closest in time.
Y. Xu, H. Su, C. Xing, B. Mi, Q. Liu, W. Shi, B. Hui, F. Zhou, Y. Liu, T. Xie, Z. Cheng, S. Zhao, L. Kong, B. Wang, C. Xiong, and T. Yu, “Lemur: Harmonizing natural language and code for language agents,” 2023
2023
Closest in time.
“Smartthings smartapp documentation,” https://developer.smartthings.com/docs/connected-services/create-a-smartapp
2023
Closest in time.
S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” arXiv preprint arXiv:2305.10601 , 2023

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

L. Wang, C. Ma, X. Feng, Z. Zhang, H. ran Yang, J. Zhang, Z.-Y. Chen, J. Tang, X. Chen, Y. Lin, W. X. Zhao, Z. Wei, and J. rong Wen, “A survey on large language model based autonomous agents,” ArXiv , vol. abs/2308.11432, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:261064713

Original

2023

Cited alongside, same era.

E. King, H. Yu, S. Lee, and C. Julien, “Sasha: creative goal-oriented reasoning in smart homes with large language models,” arXiv preprint arXiv:2305.09802 , 2023

Original

2023

Cited alongside, same era.

Y. Zhu, H. Yuan, S. Wang, J. Liu, W. Liu, C. Deng, Z. Dou, and J.-R. Wen, “Large language models for information retrieval: A survey,” arXiv preprint arXiv:2308.07107 , 2023

Original

2023

Cited alongside, same era.

W. Zhong, L. Guo, Q. Gao, and Y. Wang, “Memorybank: Enhancing large language models with long-term memory,” arXiv preprint arXiv:2305.10250 , 2023

Original

2023

Cited alongside, same era.

N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the middle: How language models use long contexts,” arXiv preprint arXiv:2307.03172 , 2023

Original

2023

Cited alongside, same era.

Zapier, “Zapier,” https://actions.zapier.com/

Cited in the paper.

H. Chase, “Langchain,” https://github.com/langchain-ai/langchain

Cited in the paper.

Original

2023

Closest in time.

S. G. Patil, T. Zhang, X. Wang, and J. E. Gonzalez, “Gorilla: Large language model connected with massive apis,” arXiv preprint arXiv:2305.15334 , 2023

Original

2023

Closest in time.

Y. Qin, S. Liang, Y. Ye, K. Zhu, L. Yan, Y. Lu, Y. Lin, X. Cong, X. Tang, B. Qian, S. Zhao, R. Tian, R. Xie, J. Zhou, M. Gerstein, D. Li, Z. Liu, and M. Sun, “Toolllm: Facilitating large language models to master 16000+ real-world apis,” 2023

2023

Closest in time.

Y. Ge, W. Hua, J. Ji, J. Tan, S. Xu, and Y. Zhang, “Openagi: When llm meets domain experts,” arXiv preprint arXiv:2304.04370 , 2023

Original

2023

Closest in time.