## 2 Language Capacity of GPT-4o
### Reasoning  
Human intellect is remarkably characterized by reasoning, which is described as an activity of methodically and logically thinking about a subject [39]. Reasoning enables humans to come to conclusions or make decisions by using previous experiences and data gathered, thus extending one's knowledge of the world and releasing the possibility for innovation and development. In recent times, AI has made significant advancements in narrowing the gap between human and machine intellect through the use of Natural Language Processing (NLP) and LLMs, which have established remarkable reasoning abilities. In this section, the authors assess the reasoning capacity of the most recent GPT-4o model via a manual technical evaluation through a sequence of question-answering tasks. The model will answer a range of logical reasoning tasks in different types, including deductive, inductive, and abductive reasoning, as shown in Figure 6. Starting with a broad principle or assumption and applying it to produce predictions or draw conclusions, deductive reasoning takes a top-down method [40]. By contrast, inductive reasoning uses a bottom-up methodology to deduce broad principles or conclusions from observations or data [41]. In abductive reasoning, theories or explanations are developed from little, ambiguous, or incomplete data [42]. With all these assessments based on the model, this article stands to obtain an understanding of the reasoning capacity of the GPT-4o model in various settings. In this subsection, we assess the performance of GPT-4o on five datasets that include all of the aforementioned types of reasoning, as illustrated in Figure 7. To evaluate the deductive reasoning ability, two datasets were utilized, namely EntailmentBank [43] and bAbI (task 15) [44]. Similarly, to assess the capability of inductive reasoning, we employed two datasets, CLUTRR [45] and bAbI (task 16) [44]. For abductive reasoning, we use the \(\alpha\)NLI dataset [46]. Adhering to the methods of [PERSON] et al. (2023), we randomly selected 30 samples from the test set for both bAbI (task 16) and CLUTRR datasets. The bAbI (task 16) dataset focuses on basic induction, where the model must derive a general rule or principle from a set of given premises. The CLUTRR dataset consists of semi-artificial stories describing hypothetical families, challenging the model to deduce relationships between family members that are not explicitly stated in the text. Using the methods of [PERSON] et al. [47] again, our evaluation encompassed the same set of 30 randomly chosen samples from each evaluation dataset. The observations are selected from 10 samples from each of the training-easy, train-medium, and train-hard sets of the \(\alpha\)NLI dataset. Concurrently, a total of 30 samples are drawn from the test set for the bAbI (task 15), bAbI (task 16), CLUTRR, and EntailmentBank datasets. In accordance with [PERSON] et al. [47], we utilized the identical set of proven and effective prompts that were implemented in their assessment to evaluate the capabilities of the model. Figure 6: Types of reasoning. The evaluation results showcase the remarkable reasoning abilities of GPT-4o in all three domains, as indicated in Table 6. GPT-4o demonstrated exceptional performance in deductive reasoning by achieving nearly flawless scores on both bAbI (task 15) and Entailment-Bank. It outperformed ChatGPT-3.5 and performed at the same level as ChatGPT-4 [47]. GPT-4o achieved flawless results in the inductive reasoning tasks, scoring perfectly on bAbI (task 16) and achieving a score of 17 out of 30 on CLUTRR. It outperformed both ChatGPT-3.5 and ChatGPT-4. GPT-4o achieved a score of 27 out of 30 on \(\alpha\)NLI, surpassing the performance of its previous versions in abductive reasoning. To ensure a fair comparison, we applied the same evaluation criteria and prompts across all models (GPT-3.5, GPT-4, and GPT-4o), mitigating potential subjective biases in the assessment process. The results underscore GPT-4o's superior reasoning capabilities compared to its predecessors.