Entity resolution (ER) is an important data integration task with a wide
spectrum of applications. The state-of-the-art solutions on ER rely on
pre-trained language models (PLMs), which require fine-tuning on a lot of
labeled matching/non-matching entity pairs. Recently, large languages models
(LLMs), such as GPT-4, have shown the ability to perform many tasks without
tuning model parameters, which is known as in-context learning (ICL) that
facilitates effective learning from a few labeled input context demonstrations.
However, existing ICL approaches to ER typically necessitate providing a task
description and a set of demonstrations for each entity pair and thus have
limitations on the monetary cost of interfacing LLMs. To address the problem,
in this paper, we provide a comprehensive study to investigate how to develop a
cost-effective batch prompting approach to ER. We introduce a framework BATCHER
consisting of demonstration selection and question batching and explore
different design choices that support batch prompting for ER. We also devise a
covering-based demonstration selection strategy that achieves an effective
balance between matching accuracy and monetary cost. We conduct a thorough
evaluation to explore the design space and evaluate our proposed strategies.
Through extensive experiments, we find that batch prompting is very
cost-effective for ER, compared with not only PLM-based methods fine-tuned with
extensive labeled data but also LLM-based methods with manually designed
prompting. We also provide guidance for selecting appropriate design choices
for batch prompting.

综合研究了如何开发一种经济高效的批量提示方法来进行实体解析，通过引入批处理和选择演示来支持批量提示，并探索了不同的设计选择。通过广泛的实验证明，与基于 PLM 的方法和手动设计提示的 LLM-based 方法相比，批量提示对于实体解析非常具有成本效益。

面向实体解析的经济高效上下文学习方法探索

Cost-Effective In-Context Learning for Entity Resolution: A Design Space  Exploration

Performing inference on hundreds of thousands of samples with large language
models (LLMs) can be computationally and financially costly. We propose batch
prompting, a simple alternative prompting approach that enables the LLM to run
inference in batches, instead of one sample at a time. Our method reduces both
token and time costs while retaining downstream performance. We theoretically
demonstrate that under a few-shot in-context learning setting, the inference
costs decrease almost inverse linearly with the number of samples in each
batch. We extensively validate the effectiveness of batch prompting on ten
datasets across commonsense QA, arithmetic reasoning, and NLI/NLU: batch
prompting significantly~(up to $5\times$ with six samples in batch) reduces the
LLM (Codex) inference token and time costs while achieving better or comparable
performance. Our analysis shows that the number of samples in each batch and
the complexity of tasks affect its performance. Further, batch prompting can be
applied across different LLMs and reasoning methods.

本文提出了批处理提示的简单替代提示方法，可以在批量中运行 LLM 的推理，而不是逐个样本。我们在许多数据集上验证了该方法的有效性，证明其可以同时减少时间和代币成本，并提高性能。