LG's AI Beats DeepSeek?! I Put EXAONE Deep to the TEST!

Introduction

In this tutorial, we will explore the performance of LG's EXAONE Deep AI model as it competes against established models like DeepSeekR1 and Qwen (QWQ). We will analyze how EXAONE Deep handles a logic puzzle, specifically the ice cube problem, and assess its reasoning capabilities. This guide will provide insights into the testing process and highlight key takeaways from the evaluation.

Step 1: Set Up Your Testing Environment

To begin testing the EXAONE Deep model, follow these steps:

1. Install LM Studio

   • Visit the LM Studio website and download the latest version.
   • Follow the installation instructions specific to your operating system (Windows, macOS, or Linux).

2. Prepare Your AI Models

   • Ensure you have access to the EXAONE Deep, DeepSeekR1, and Qwen (QWQ) models.
   • Load each model into LM Studio according to the provided documentation.

3. Familiarize Yourself with the Interface

   • Take some time to explore LM Studio’s features, including how to input queries and view output.

Step 2: Formulate the Logic Puzzle

For this test, we will use the ice cube problem, which is a classic logic puzzle designed to evaluate reasoning skills. Here’s how to set it up:

1. Define the Problem

   • Present the ice cube scenario clearly to each model. For example, "If an ice cube melts in a glass of water, what happens to the water level?"

2. Input the Problem into LM Studio

   • Use the input field to submit the puzzle to each AI model one at a time.
   • Monitor the response time and the reasoning process in real-time.

Step 3: Analyze Model Performance

Once you have run the logic puzzle through each model, it’s time to analyze the results:

1. Evaluate Responses

   • Compare each model's answer to the expected logical conclusion.
   • Note any discrepancies in reasoning or unexpected responses.

2. Assess Reasoning Process

   • Observe how each model articulates its thought process.
   • Pay attention to the clarity and coherence of the explanations provided.

3. Document Findings

   • Create a comparison chart to summarize the strengths and weaknesses of each model based on the logic puzzle results.

Step 4: Reflect on Initial Impressions

After conducting the tests, consider the following:

1. Performance Insights

   • Did EXAONE Deep outperform the others as claimed?
   • Were there any surprising behaviors observed during the test?

2. Potential Improvements

   • Identify areas where EXAONE Deep may need refinement, particularly in logical deduction skills.

3. Future Testing

   • Plan additional tests with different types of puzzles or queries to further evaluate each model's capabilities.

Conclusion

In this tutorial, we set up a testing environment to evaluate LG's EXAONE Deep model against DeepSeekR1 and Qwen (QWQ) using a logic puzzle. By following the outlined steps, you can assess the reasoning abilities of these AI models and draw conclusions about their performance. Keep exploring different scenarios and challenges to gain deeper insights into the strengths and limitations of each model in real-world applications.