# Automated Assessment of Inferences Using Pre-Trained Language Models
## 1 Introduction  
### Automating the Assessment of Reading Comprehension Skills  
Natural language processing (NLP) can improve productivity in many areas. This is especially true for organizations that deal with large volumes of textual data [1]. For example, NLP helps users to identify and extract key entities, facts, and relationships from text to build knowledge bases [2]. These knowledge bases become valuable resources for quick information access and decision making. In addition, in customer service, sentence classification can be used to automatically categorize customer inquiries, complaints, or feedback [3]. This enables quicker routing to the appropriate department or personnel, thereby reducing response times and improving customer satisfaction. Furthermore, sentiment analysis, a special case of sentence classification, can automatically identify positive, negative, or neutral sentiments in customer feedback or social media posts [4]. This helps organizations to quickly gauge public opinion and adjust their strategies accordingly. Recent research has increasingly focused on using NLP to augment or even replace human expert judgments. In the legal field, NLP-powered tools are accelerating case preparation by efficiently sifting through large volumes of legal documents [5]. Similarly, in the financial sector, NLP-based systems are enhancing the ability of analysts to predict market trends by analyzing large and complex datasets [6], a task that is beyond human capabilities. Similarly, in education, automated essay grading systems provide instant feedback [7], thus allowing educators to spend more time on individualized instructions. This study focuses on automating the assessment of reading comprehension skills through the development and application of advanced NLP in education. Reading is a fundamental skill for the acquisition of knowledge [8]. Assessing reading skills is challenging because reading involves multiple cognitive processes, such as letter-sound correspondence, phonological memory, word recognition, sentence processing, and comprehension [9]. These individual skills are assessed using standardized tests [10]. The objectivityof standardized tests makes it possible to compare the reading skills of students from different backgrounds using the same criteria. However, standardized tests often focus on specific types of reading skills, potentially neglecting broader aspects of literacy, such as critical thinking and engagement with diverse background knowledge [11]. To gain a holistic understanding of a student's reading skills, the interpretation of test results and personalized feedback from human experts are essential. Machine learning models are actively used to improve the accuracy of reading level assessments. In [12], [PERSON] and [PERSON] used support vector machines to combine lexical and syntactic features of a given text to assess its reading level. The corpus used for this study was _Weekly Readers_, which consisted of 2400 articles from an educational magazine designed for children of ages 7-10 in the United States. Their model outperformed the traditional Flesch-Kincaid score [13], which is based on sentence length and the average syllable count. In [14], [PERSON] et al. used latent semantic analysis (LSA) [15] and word matching to first identify students' reading strategies and then estimate their reading comprehension scores. The correlation between predicted and actual reading scores was low (<0.5), indicating that estimating reading comprehension skills is a difficult task. In [16], [PERSON] et al. used a linguistic measure of coherence, called the Coh-Matrix [17], and LSA to assess the reading comprehension of high school students. Their predictions were compared with standardized scores, using the Gates-MacGinitie reading skill test, level 10/12 [18], and the correlation between them was low (<0.5). However, existing studies using machine learning models combine known linguistic features to improve the overall accuracy of reading assessments.