Recently, instruction-following Large Language Models (LLMs) , represented by
ChatGPT, have exhibited exceptional performance in general Natural Language
Processing (NLP) tasks. However, the unique characteristics of E-commerce data
pose significant challenges to general LLMs. An LLM tailored specifically for
E-commerce scenarios, possessing robust cross-dataset/task generalization
capabilities, is a pressing necessity. To solve this issue, in this work, we
proposed the first e-commerce instruction dataset EcomInstruct, with a total of
2.5 million instruction data. EcomInstruct scales up the data size and task
diversity by constructing atomic tasks with E-commerce basic data types, such
as product information, user reviews. Atomic tasks are defined as intermediate
tasks implicitly involved in solving a final task, which we also call
Chain-of-Task tasks. We developed EcomGPT with different parameter scales by
training the backbone model BLOOMZ with the EcomInstruct. Benefiting from the
fundamental semantic understanding capabilities acquired from the Chain-of-Task
tasks, EcomGPT exhibits excellent zero-shot generalization capabilities.
Extensive experiments and human evaluations demonstrate that EcomGPT
outperforms ChatGPT in term of cross-dataset/task generalization on E-commerce
tasks.

为了解决电子商务数据对通用大语言模型的挑战问题，在本文中我们提出了第一个电子商务指令数据集 EcomInstruct，并通过构建基于电子商务基本数据类型的原子任务来扩大数据规模和任务多样性。通过在 EcomInstruct 上训练背骨模型 BLOOMZ，我们开发了具有不同参数规模的 EcomGPT。EcomGPT 在零样本泛化能力方面表现出色。广泛的实验和人工评估表明，EcomGPT 在电子商务任务的跨数据集 / 任务泛化方面优于 ChatGPT。

EcomGPT：使用任务链调整的大型语言模型，适用于电子商务

EcomGPT: Instruction-tuning Large Language Model with Chain-of-Task  Tasks for E-commerce

The categorization of massive e-Commerce data is a crucial, well-studied
task, which is prevalent in industrial settings. In this work, we aim to
improve an existing product categorization model that is already in use by a
major web company, serving multiple applications. At its core, the product
categorization model is a text classification model that takes a product title
as an input and outputs the most suitable category out of thousands of
available candidates. Upon a closer inspection, we found inconsistencies in the
labeling of similar items. For example, minor modifications of the product
title pertaining to colors or measurements majorly impacted the model's output.
This phenomenon can negatively affect downstream recommendation or search
applications, leading to a sub-optimal user experience.
To address this issue, we propose a new framework for consistent text
categorization. Our goal is to improve the model's consistency while
maintaining its production-level performance. We use a semi-supervised approach
for data augmentation and presents two different methods for utilizing
unlabeled samples. One method relies directly on existing catalogs, while the
other uses a generative model. We compare the pros and cons of each approach
and present our experimental results.

本文提出了一种新框架来解决电子商务数据分类的问题，采用半监督学习方法进行数据增强，提高模型的一致性并保持其生产水平性能。

电子商务中使用数据增强实现一致的文本分类

Consistent Text Categorization using Data Augmentation in e-Commerce

With the recent advances in Reinforcement Learning (RL), there have been
tremendous interests in employing RL for recommender systems. However, directly
training and evaluating a new RL-based recommendation algorithm needs to
collect users' real-time feedback in the real system, which is time and efforts
consuming and could negatively impact on users' experiences. Thus, it calls for
a user simulator that can mimic real users' behaviors where we can pre-train
and evaluate new recommendation algorithms. Simulating users' behaviors in a
dynamic system faces immense challenges -- (i) the underlining item
distribution is complex, and (ii) historical logs for each user are limited. In
this paper, we develop a user simulator base on Generative Adversarial Network
(GAN). To be specific, the generator captures the underlining distribution of
users' historical logs and generates realistic logs that can be considered as
augmentations of real logs; while the discriminator not only distinguishes real
and fake logs but also predicts users' behaviors. The experimental results
based on real-world e-commerce data demonstrate the effectiveness of the
proposed simulator.

本文介绍了一种基于生成对抗网络的用户模拟器，可以用于预先训练和评估新的基于强化学习的推荐算法，并使用真实世界的电子商务数据进行实验验证。

基于强化学习的推荐系统中环境模拟的研究

Toward Simulating Environments in Reinforcement Learning Based  Recommendations

In this paper, we propose an effective way for biasing the attention
mechanism of a sequence-to-sequence neural machine translation (NMT) model
towards the well-studied statistical word alignment models. We show that our
novel guided alignment training approach improves translation quality on
real-life e-commerce texts consisting of product titles and descriptions,
overcoming the problems posed by many unknown words and a large type/token
ratio. We also show that meta-data associated with input texts such as topic or
category information can significantly improve translation quality when used as
an additional signal to the decoder part of the network. With both novel
features, the BLEU score of the NMT system on a product title set improves from
18.6 to 21.3%. Even larger MT quality gains are obtained through domain
adaptation of a general domain NMT system to e-commerce data. The developed NMT
system also performs well on the IWSLT speech translation task, where an
ensemble of four variant systems outperforms the phrase-based baseline by 2.1%
BLEU absolute.

本文提出了一种有效的方法来偏置序列到序列神经机器翻译模型（NMT）的注意机制，使其趋向于研究过的词对齐模型。我们展示了我们的新型引导对齐训练方法如何在真实生活的电商文本翻译中提高了翻译质量，克服了许多未知单词和大型类型 / 令牌比。我们还表明，与输入文本相关的元数据（例如主题或类别信息）作为网络解码器部分的附加信号可显着提高翻译质量。有了这两个新功能，NMT 系统在一个产品标题集上的 BLEU 分数从 18.6 提高到 21.3％。通过将通用领域 NMT 系统进行领域适应也可获得更大的机器翻译质量提升。这个开发的 NMT 系统在 IWSLT 语音翻译任务中也表现良好，其中四个变体系统的集合比基线的基于短语的系统的 BLEU 分数提高了 2.1％。