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    <abstract>Language models are one of the biggest game changers in downstream NLP applications, especially in conversational agents. In spite of their awesome capabilities to generated responses to solve the inquireis, there are still some big challenges to using them. One challenge is how to enable the LLMs to use the private internal data to solve inquires. And secondly, how to keep the LLMs updated with newly incoming data without the burden of fine-tuning as it is not only expensive but also not an available option for some commercial LLMs, such as ChatGPT. In this work, we propose Semantic In-Context Learning (S-ICL) to address the aforementioned challenges. Our approach was participated in the BEA 2023 shared task and ended up having the fourth place in both development and evaluation phases.</abstract>
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%0 Conference Proceedings
%T Empowering Conversational Agents using Semantic In-Context Learning
%A Omidvar, Amin
%A An, Aijun
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F omidvar-an-2023-empowering
%X Language models are one of the biggest game changers in downstream NLP applications, especially in conversational agents. In spite of their awesome capabilities to generated responses to solve the inquireis, there are still some big challenges to using them. One challenge is how to enable the LLMs to use the private internal data to solve inquires. And secondly, how to keep the LLMs updated with newly incoming data without the burden of fine-tuning as it is not only expensive but also not an available option for some commercial LLMs, such as ChatGPT. In this work, we propose Semantic In-Context Learning (S-ICL) to address the aforementioned challenges. Our approach was participated in the BEA 2023 shared task and ended up having the fourth place in both development and evaluation phases.
%R 10.18653/v1/2023.bea-1.62
%U https://aclanthology.org/2023.bea-1.62
%U https://doi.org/10.18653/v1/2023.bea-1.62
%P 766-771

Markdown (Informal)

[Empowering Conversational Agents using Semantic In-Context Learning](https://aclanthology.org/2023.bea-1.62) (Omidvar & An, BEA 2023)

• Empowering Conversational Agents using Semantic In-Context Learning (Omidvar & An, BEA 2023)

ACL

• Amin Omidvar and Aijun An. 2023. Empowering Conversational Agents using Semantic In-Context Learning. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 766–771, Toronto, Canada. Association for Computational Linguistics.