基于复杂度的CSFI问题实例Transformers划分
Complexity-based partitioning of CSFI problem instances with Transformers
摘要：
本文提出了一种两步的方法来划分合取范式（CNF）句法公式同构问题（CSFI）的实例到不同复杂度的组。首先，我们建立一个模型，基于Transformer架构，试图解决CSFI问题的实例。然后，我们利用这种模型的错误和训练第二个基于transformer的模型划分成不同复杂度的问题实例组，从而检测出可以解决，而不使用太昂贵的资源。我们评估所提出的方法在一个伪随机生成的数据集，并获得有希望的结果。最后，我们讨论了将这种方法扩展到基于相同类型的文本表示的其他问题的可能性。
英文摘要：
In this paper, we propose a two-steps approach to partition instances of the Conjunctive Normal Form (CNF) Syntactic Formula Isomorphism problem (CSFI) into groups of different complexity. First, we build a model, based on the Transformer architecture, that attempts to solve instances of the CSFI problem. Then, we leverage the errors of such model and train a second Transformer-based model to partition the problem instances into groups of different complexity, thus detecting the ones that can be solved without using too expensive resources. We evaluate the proposed approach on a pseudo-randomly generated dataset and obtain promising results. Finally, we discuss the possibility of extending this approach to other problems based on the same type of textual representation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
