Multi-span answer extraction, also known as the task of multi-span question
answering (MSQA), is critical for real-world applications, as it requires
extracting multiple pieces of information from a text to answer complex
questions. Despite the active studies and rapid progress in English MSQA
research, there is a notable lack of publicly available MSQA benchmark in
Chinese. Previous efforts for constructing MSQA datasets predominantly
emphasized entity-centric contextualization, resulting in a bias towards
collecting factoid questions and potentially overlooking questions requiring
more detailed descriptive responses. To overcome these limitations, we present
CLEAN, a comprehensive Chinese multi-span question answering dataset that
involves a wide range of open-domain subjects with a substantial number of
instances requiring descriptive answers. Additionally, we provide established
models from relevant literature as baselines for CLEAN. Experimental results
and analysis show the characteristics and challenge of the newly proposed CLEAN
dataset for the community. Our dataset, CLEAN, will be publicly released at
zhiyiluo.site/misc/clean_v1.0_ sample.json.

CLEAN 是一个综合的中国多段落问答数据集，它包含了广泛的开放领域主题，并需要大量实例来回答描述性问题。

一个带有多段答案的开放领域问答数据集

A Dataset of Open-Domain Question Answering with Multiple-Span Answers

Whereas the recent emergence of large language models (LLMs) like ChatGPT has
exhibited impressive general performance, it still has a large gap with
fully-supervised models on specific tasks such as multi-span question
answering. Previous researches found that in-context learning is an effective
approach to exploiting LLM, by using a few task-related labeled data as
demonstration examples to construct a few-shot prompt for answering new
questions. A popular implementation is to concatenate a few questions and their
correct answers through simple templates, informing LLM of the desired output.
In this paper, we propose a novel way of employing labeled data such that it
also informs LLM of some undesired output, by extending demonstration examples
with feedback about answers predicted by an off-the-shelf model, e.g., correct,
incorrect, or incomplete. Experiments on three multi-span question answering
datasets as well as a keyphrase extraction dataset show that our new prompting
strategy consistently improves LLM's in-context learning performance.

本文提出了一种新的提示策略，通过使用带有反馈的演示示例来提高大型语言模型（LLM）的上下文学习性能，该策略在三个多跨度问题回答数据集以及一个关键字提取数据集上进行实验证明了其一致的性能提高。