# Exploring the Limits of Large Language Models' Ability to Distinguish Between Objects
## 5 Discussion  
### Object Classification  
Figure 9 illustrates how the model responses are categorized for a question (Q1), serving as an example of the overall approach used for all queries. Rather than displaying every question and answer in full, we group the responses into distinct stages--\"Incorrect output\", \"Correct for step 1\", and \"Correct for steps 1 and 2\"--to highlight how the models refine their answers when given additional instructions or follow-up prompts. While this figure focuses on Q1, the same categorization method is applied throughout our study to allow for a consistent comparison of how each model's reasoning evolves across different types of questions.  
In Q2, the model must classify each item strictly as \"carrot\", \"orange\", \"bell pepper\", or \"hand\" with no explicit call to distinguish real from fake. Consequently, it often responds with statements like \"This object is a carrot\", omitting any mention of authenticity. As a result, if the model fails to label a fake item as \"fake\", we count it as having classified that object as \"real\". Because many items are, in fact, artificial, the final accuracy averages around 50%. Thus, while the model's basic object identification (e.g., recognizing a carrot shape) may look accurate, it does not address real-versus-fake question--highlighting the challenge of incorporating authenticity judgments into what otherwise appears to be straightforward classification.  
A key reason for the lower accuracy on Q3, compared to Q1 is the more complex and context-heavy reasoning it requires. While Q1 focuses on straightforward identification (\"What is this object?\"), Q3 compels the model to infer additional attributes, synthesize multiple clues, and explicitly determine whether an object is fake. Consequently, as shown in Table 3, GPT-4o's accuracy on Q3 Step 1 (Single Object) dips to about 95.77%, down from 98.59%, on Q1 Step 1--a modest drop that becomes a 10-15% decline for many smaller models. This underlines how tasks involving subtler distinctions or extra context (e.g., identifying fake details) present a substantially higher cognitive load for vision  
Figure 9: Object classification Q1 results (red: incorrect object classification; yellow: correct object classification but cannot distinguish between real and fake; green: correct object classification and also distinguishes between real and fake).  
language models. Furthermore, Q3 explicitly calls for fake objects to be labeled as \"fake\", so models that merely note an object type (e.g., \"carrot\") without acknowledging its authenticity risk being scored incorrectly.  
Turning to Q4, Table 3 reveals that single-object performance (Q4-1) is generally higher than in previous tasks, likely because Q4 provides clearer textual cues for both classification and authenticity. In contrast, Q2 and Q3 often involve more nuanced features or multiple attributes, compounding the difficulty of deciding \"real vs. fake\". By focusing on a single, distinctly defined object, Q4-1 reduces ambiguity, and even smaller models achieve their strongest results.  
Nevertheless, when multiple objects appear in one image, performance universally declines. An example of results is shown in Figure 10. As seen under the \"Multi-Object\" columns in Tables 2 and 3, some models that achieve near-perfect scores in single-object tasks drop sharply when forced to identify and assess several items simultaneously. Importantly, the second stage of our evaluation--Step 2--applies additional correctness checks, further magnifying any errors. Even GPT-4o--consistently strong in single-object recognition--experiences a noticeable dip. In summary, these findings emphasize how handling multiple objects, each with its own attributes and potential authenticity considerations, pushes the models' capacities beyond simpler classification demands, revealing ongoing limitations in their current reasoning capabilities.