  Language Models have previously shown strong negotiation capabilities in
closed domains where the negotiation strategy prediction scope is constrained
to a specific setup. In this paper, we first show that these models are not
generalizable beyond their original training domain despite their wide-scale
pretraining. Following this, we propose an automated framework called GNOME,
which processes existing human-annotated, closed-domain datasets using Large
Language Models and produces synthetic open-domain dialogues for negotiation.
GNOME improves the generalizability of negotiation systems while reducing the
expensive and subjective task of manual data curation. Through our experimental
setup, we create a benchmark comparing encoder and decoder models trained on
existing datasets against datasets created through GNOME. Our results show that
models trained on our dataset not only perform better than previous state of
the art models on domain specific strategy prediction, but also generalize
better to previously unseen domains.
