Complexity-based partitioning of CSFI problem instances with Transformers

TL;DR

This paper introduces a two-step Transformer-based method to classify CSFI problem instances by complexity, aiming to optimize resource usage in solving these instances.

Contribution

The paper presents a novel two-stage Transformer approach for partitioning CSFI instances by complexity, improving resource allocation for problem-solving.

Findings

Promising results on a pseudo-random dataset

Effective detection of low-resource complexity instances

Potential for extension to other textual problems

Abstract

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.