In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models. (arXiv:2305.03573v1 [cs.CL])

The phenomena of in-context learning has typically been thought of as
"learning from examples". In this work which focuses on Machine Translation, we
present a perspective of in-context learning as the desired generation task
maintaining coherency with its context, i.e., the prompt examples. We first
investigate randomly sampled prompts across 4 domains, and find that
translation performance improves when shown in-domain prompts. Next, we
investigate coherency for the in-domain setting, which uses prompt examples
from a moving window. We study …

arxiv context examples language language models large language models machine machine translation perspective prompt study thought translation work