Entity Matching is the task of deciding if two entity descriptions refer to
the same real-world entity. State-of-the-art entity matching methods often rely
on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks
of using these models for entity matching are that (i) the models require
significant amounts of fine-tuning data for reaching a good performance and
(ii) the fine-tuned models are not robust concerning out-of-distribution
entities. In this paper, we investigate using ChatGPT for entity matching as a
more robust, training data-efficient alternative to traditional Transformer
models. We perform experiments along three dimensions: (i) general prompt
design, (ii) in-context learning, and (iii) provision of higher-level matching
knowledge. We show that ChatGPT is competitive with a fine-tuned RoBERTa model,
reaching an average zero-shot performance of 83% F1 on a challenging matching
task on which RoBERTa requires 2000 training examples for reaching a similar
performance. Adding in-context demonstrations to the prompts further improves
the F1 by up to 5% even using only a small set of 20 handpicked examples.
Finally, we show that guiding the zero-shot model by stating higher-level
matching rules leads to similar gains as providing in-context examples.

本文探讨使用 ChatGPT 作为传统 Transformer 模型的更健壮、训练数据更有效的替代方法，对实体匹配任务进行实验，证明 ChatGPT 表现竞争力与经过 fine-tuned 的 RoBERTa 模型相当，达到 83％的 F1 零次训练，同时使用很少一部分的在上下文中的示例以及提供高级匹配规则可在零次训练下获得类似的增益。

使用 ChatGPT 进行实体匹配

Using ChatGPT for Entity Matching

What can pre-trained multilingual sequence-to-sequence models like mBART
contribute to translating low-resource languages? We conduct a thorough
empirical experiment in 10 languages to ascertain this, considering five
factors: (1) the amount of fine-tuning data, (2) the noise in the fine-tuning
data, (3) the amount of pre-training data in the model, (4) the impact of
domain mismatch, and (5) language typology. In addition to yielding several
heuristics, the experiments form a framework for evaluating the data
sensitivities of machine translation systems. While mBART is robust to domain
differences, its translations for unseen and typologically distant languages
remain below 3.0 BLEU. In answer to our title's question, mBART is not a
low-resource panacea; we therefore encourage shifting the emphasis from new
models to new data.

通过在 10 种语言上进行完备实验，考虑了五个因素：（1）微调数据量，（2）微调数据中的噪声，（3）模型预训练数据量，（4）领域不匹配的影响以及（5）语言类型学，我们试图确定预训练多语言序列到序列模型（如 mBART）对翻译低资源语言有何贡献，并提出了许多启发式方法。实验结果显示，mBART 对领域差异具有鲁棒性，但对未知和语言类型学差异较大的语言的翻译仍低于 3.0 BLEU。因此，我们鼓励将重点从新模型转向新数据。