With tremendous efforts on developing effective e-commerce models,
conventional e-commerce models show limited success in generalist e-commerce
modeling, and suffer from unsatisfactory performance on new users and new
products - a typical out-of-domain generalization challenge. Meanwhile, large
language models (LLMs) demonstrate outstanding performance in generalist
modeling and out-of-domain generalizability in many fields. Toward fully
unleashing their power for e-commerce, in this paper, we construct ECInstruct,
the first open-sourced, large-scale, and high-quality benchmark instruction
dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of
e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive
experiments and evaluation demonstrate that eCeLLM models substantially
outperform baseline models, including the most advanced GPT-4, and the
state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM
exhibits excellent generalizability to out-of-domain settings, including unseen
products and unseen instructions, highlighting its superiority as a generalist
e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great
potential in empowering versatile and effective LLMs for e-commerce. ECInstruct
and eCeLLM models are publicly accessible through
this https URL

该研究构建了 ECInstruct 数据集，发展了一系列针对电子商务的大型语言模型（eCeLLM）。综合实验和评估结果表明，eCeLLM 模型在领域内优于基线模型，包括最先进的 GPT-4 和最新的任务特定模型，并且在领域外的情景中表现出卓越的泛化能力，突显其作为一种通用电子商务模型的优越性。ECInstruct 数据集和 eCeLLM 模型潜力巨大，可以为电子商务领域的多功能和有效的语言模型提供支持。

eCeLLM：从大规模、高质量的指导数据推广用于电子商务的大型语言模型

eCeLLM: Generalizing Large Language Models for E-commerce from  Large-scale, High-quality Instruction Data

As NLP models are increasingly deployed in socially situated settings such as
online abusive content detection, it is crucial to ensure that these models are
robust. One way of improving model robustness is to generate counterfactually
augmented data (CAD) for training models that can better learn to distinguish
between core features and data artifacts. While models trained on this type of
data have shown promising out-of-domain generalizability, it is still unclear
what the sources of such improvements are. We investigate the benefits of CAD
for social NLP models by focusing on three social computing constructs --
sentiment, sexism, and hate speech. Assessing the performance of models trained
with and without CAD across different types of datasets, we find that while
models trained on CAD show lower in-domain performance, they generalize better
out-of-domain. We unpack this apparent discrepancy using machine explanations
and find that CAD reduces model reliance on spurious features. Leveraging a
novel typology of CAD to analyze their relationship with model performance, we
find that CAD which acts on the construct directly or a diverse set of CAD
leads to higher performance.

研究如何提高自然语言处理模型的稳健性，发现使用对抗性生成数据可以降低模型在错误特征上的依赖，从而提高模型在领域外的泛化性能。