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.

通过使用大型语言模型处理现存的人工标注的闭域数据集，本研究论文提出了一个自动化框架 GNOME，用于生成合成的开域对话进行谈判，从而提高了谈判系统的泛化能力，并减少了手动数据整理的昂贵性和主观性任务。通过实验设置，我们创建了一个基准，将在现有数据集上训练的编码器和解码器模型与通过 GNOME 创建的数据集进行比较。结果显示，我们的数据集上训练的模型不仅在特定领域策略预测方面表现优于先前的最新模型，而且对以前未见过的领域有更好的泛化能力。