论文标题
M2D2：大量多域语言建模数据集
M2D2: A Massively Multi-domain Language Modeling Dataset
论文作者
论文摘要
我们提出了M2D2，这是一种用于研究语言模型（LMS）中的域适应性的细粒度，大量多域语体。 M2D2由8.5b代币和跨度145个领域组成，这些结构域是从Wikipedia和语义学者中提取的。使用源自Wikipedia和Arxiv类别的本体论，我们将每个数据源中的域组织分为22组。这个两级层次结构可以研究适应后域之间的关系及其对域内和室外性能的影响。我们还对LMS中有效域适应的性质进行了许多见解，作为新型研究类型M2D2的示例。为了提高内域性能，我们显示了沿域层次结构适应LM的好处；适应较少量的细粒域特异性数据可能会导致比大量弱相关数据更大的内域性能增长。我们进一步证明了本体内部和跨本体内部和跨域的概括之间的权衡，以及域之间的台面性能与词汇叠加之间的强烈相关性。
We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs). M2D2 consists of 8.5B tokens and spans 145 domains extracted from Wikipedia and Semantic Scholar. Using ontologies derived from Wikipedia and ArXiv categories, we organize the domains in each data source into 22 groups. This two-level hierarchy enables the study of relationships between domains and their effects on in- and out-of-domain performance after adaptation. We also present a number of insights into the nature of effective domain adaptation in LMs, as examples of the new types of studies M2D2 enables. To improve in-domain performance, we show the benefits of adapting the LM along a domain hierarchy; adapting to smaller amounts of fine-grained domain-specific data can lead to larger in-domain performance gains than larger amounts of weakly relevant data. We further demonstrate a trade-off between in-domain specialization and out-of-domain generalization within and across ontologies, as well as a strong correlation between out-of-domain performance and lexical overlap between domains.
