March 27, 2024, 4:48 a.m. | Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman, Shrikanth Narayanan

cs.CL updates on

arXiv:2403.17125v1 Announce Type: new
Abstract: In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) without updating the models' parameters, in contrast to the traditional gradient-based finetuning. The promise of ICL is that the LLM can adapt to perform the present task at a competitive or state-of-the-art level at a fraction of the cost. The ability of LLMs to perform tasks in this few-shot manner relies on their background knowledge of …

abstract arxiv context contrast emotion finetuning gradient impact in-context learning knowledge language language models large language large language models llm natural natural language paradigm parameters prior recognition tasks type

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