Empowering Conversational Agents using Semantic In-Context Learning
Amin Omidvar, Aijun An · 2023
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 inquiries, 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 finetuning 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 proposed approach participated in the BEA 2023 shared task 1 and ended up achieving the fourth place in both the development and evaluation phases.