@inproceedings{muthusamy-etal-2023-towards,
title = "Towards large language model-based personal agents in the enterprise: Current trends and open problems",
author = "Muthusamy, Vinod and
Rizk, Yara and
Kate, Kiran and
Venkateswaran, Praveen and
Isahagian, Vatche and
Gulati, Ashu and
Dube, Parijat",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.461",
doi = "10.18653/v1/2023.findings-emnlp.461",
pages = "6909--6921",
abstract = "There is an emerging trend to use large language models (LLMs) to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal. This functionality could, among other use cases, be used to build personal assistants for knowledge workers. While there are impressive demos of LLMs being used as autonomous agents or for tool composition, these solutions are not ready mission-critical enterprise settings. For example, they are brittle to input changes, and can produce inconsistent results for the same inputs. These use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, adherence to guardrails and policies, best practices for composable tool design, and the need for new metrics and benchmarks. This vision paper illustrates some examples of LLM-based autonomous agents that reason and compose tools, highlights cases where they fail, surveys some of the recent efforts in this space, and lays out the research challenges to make these solutions viable for enterprises.",
}
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%0 Conference Proceedings
%T Towards large language model-based personal agents in the enterprise: Current trends and open problems
%A Muthusamy, Vinod
%A Rizk, Yara
%A Kate, Kiran
%A Venkateswaran, Praveen
%A Isahagian, Vatche
%A Gulati, Ashu
%A Dube, Parijat
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F muthusamy-etal-2023-towards
%X There is an emerging trend to use large language models (LLMs) to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal. This functionality could, among other use cases, be used to build personal assistants for knowledge workers. While there are impressive demos of LLMs being used as autonomous agents or for tool composition, these solutions are not ready mission-critical enterprise settings. For example, they are brittle to input changes, and can produce inconsistent results for the same inputs. These use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, adherence to guardrails and policies, best practices for composable tool design, and the need for new metrics and benchmarks. This vision paper illustrates some examples of LLM-based autonomous agents that reason and compose tools, highlights cases where they fail, surveys some of the recent efforts in this space, and lays out the research challenges to make these solutions viable for enterprises.
%R 10.18653/v1/2023.findings-emnlp.461
%U https://aclanthology.org/2023.findings-emnlp.461
%U https://doi.org/10.18653/v1/2023.findings-emnlp.461
%P 6909-6921
Markdown (Informal)
[Towards large language model-based personal agents in the enterprise: Current trends and open problems](https://aclanthology.org/2023.findings-emnlp.461) (Muthusamy et al., Findings 2023)
ACL