I compared QwQ-32B-Preview to Marco-01
And here are the results
In this article, we compare the performance of two language models: QwQ-32B-Preview and Marco-01. The evaluation is focused on their capabilities in solving diverse problem types, including mathematical reasoning, logical puzzles, abstract reasoning, and contextual understanding. The aim is to determine how well each model handles complex challenges and systematically approaches the problems presented. Below are the details of their responses and a comparison of their performance across different test categories.
First off, I would like to highlight the major difference between the two models: a 32B model versus a tiny 7B model. Despite the significant difference in size, Marco-01's performance in comparison to QwQ-32B-Preview is notable.
Comparison Table
Test Category | Question Description | QwQ-32B-Preview Result | Marco-01 Result | Evaluation |
|---|---|---|---|---|
Mathematical Reasoning Test | Solve a system of linear equations involving multiple steps and logical sequence. | Correct solution found through a detailed step-by-step process, concluding with consistent values for 'a' and 'b'. | Solution found through both substitution and elimination methods; results match QwQ-32B-Preview. | Tie |
Logic Puzzle Test | Solve a classic logical puzzle involving mislabeled boxes of apples and oranges, requiring a systematic pick to determine true labels. | Presented multiple approaches to solve the puzzle; provided a logical deduction strategy, eventually concluding the correct labels for all boxes. | Presented a similar solution but with slightly more direct reasoning and fewer alternative explanations. | Tie |
Abstract Reasoning Test | Identify and predict the next term in a sequence of perfect squares. | Correctly identified the sequence as perfect squares and predicted the next term. Took additional steps to validate the pattern using differences between terms. | Quickly identified the sequence as perfect squares and provided the correct next term, focusing directly on recognizing the mathematical pattern. | Tie |
Contextual Understanding Test | Distribute 400 attendees optimally among three venues, with restrictions on how remaining attendees can be split if one venue reaches full capacity. | Provided a detailed breakdown of possible scenarios, considering different attendee allocations. Successfully identified an optimal distribution meeting given criteria. | Reached a similar optimal distribution but with a more straightforward explanation, focusing on the key restriction involving Venue A. | Tie |
Common-Sense Reasoning Test | Predict what happens to a wooden spoon placed in boiling water for 30 minutes, considering the properties of wood and its response to heat and moisture. | Explained the effects of boiling water on the wooden spoon, including water absorption, softening, and potential warping over time. | Provided a similar explanation but with fewer potential outcomes considered, resulting in a more succinct answer. | QwQ-32B |
This table highlights the key differences in approach and accuracy between QwQ-32B-Preview and Marco-01 for each test. Both models exhibited strengths and weaknesses, with Marco-01 providing more direct and concise reasoning in certain cases, while QwQ-32B-Preview showed more depth in exploring multiple potential approaches.
1. Mathematical Reasoning Test
Question: If 3a+5b=27 and 4b−2a=10, what are the values of a and b?
Objective: Evaluate the model’s ability to solve multiple-step algebra problems with a well-defined logical sequence.
Solution from QwQ:
\[ \boxed{a = \dfrac{29}{11},\ b = \dfrac{42}{11}} \]
Solution from Marco01:
The values of \( a \) and \( b \) that satisfy the given system of equations are approximately:
\[ a \approx 2.636 \]
\[ b \approx 3.818 \]
These solutions were found using both the substitution and elimination methods, ensuring consistency and accuracy.
2. Logic Puzzle Test
Question: You have three boxes. One contains only apples, one contains only oranges, and the third contains both apples and oranges. Each box is labeled incorrectly. You may pick one fruit from one box only. How can you determine the correct labels for all three boxes?
Objective: Assess how well the model can apply systematic reasoning to solve a classic logical puzzle.
Solution from QwQ:
\[ \boxed{\text{Pick one fruit from the box labeled "Apples and Oranges"}} \]
Solution from Marco01:
the optimal strategy is to pick one fruit from the box labeled "Apples & Oranges" to efficiently determine the correct contents of all three boxes.
3. Abstract Reasoning Test
Question: What is the next term in the series: 1, 4, 9, 16, 25, ...?
Objective: Measure the model's ability to recognize and extend a pattern, in this case, the sequence of perfect squares.
Solution from QwQ:
\[ \boxed{36} \]
Solution from Marco01:
The next term in the series is 36.
4. Contextual Understanding (Complex Prompts) Test
Question: Imagine you’re organizing a large conference with three different venues. Venue A holds 200 people, Venue B holds 150 people, and Venue C holds 100 people. You have 400 attendees. However, due to logistical issues, if Venue A is full, only half of the remaining attendees can fit into Venue B, and the rest have to go to Venue C. How do you distribute the attendees optimally among the three venues?
Objective: Test the model’s ability to understand and reason through a complex prompt with multiple variables.
Solution from QwQ:
\[ \boxed{\text{Venue A: } 150, \text{ Venue B: } 150, \text{ Venue C: } 100} \]
Solution from Marco01:
To distribute the 400 attendees optimally:
1. Check if Venue A needs to be fully occupied.
- If yes, allocate 200 attendees to Venue A, 100 to Venue B, and 100 to Venue C.
- If no, distribute attendees freely within the capacities of all venues, ensuring the total adds up to 400 without exceeding any venue's limit.
This approach ensures that resources are maximized and all attendees are accommodated appropriately based on the availability of each venue.
5. Common-Sense Reasoning Test
Question: If you put a wooden spoon in a pot of boiling water, what will happen to the spoon after 30 minutes? Why?
Objective: See how well the model can reason through a basic scenario that requires an understanding of the physical world.
Solution from QwQ:
\[ \boxed{\text{The wooden spoon will absorb water, potentially swelling or softening, and may show signs of weakening due to prolonged exposure to heat and moisture.}} \]
Solution from Marco01:
In summary, after 30 minutes in boiling water, the wooden spoon is likely to remain relatively cool to the touch, with only minor warming due to heat conduction. It may experience slight warping or bending if it absorbs significant amounts of water, depending on the specific properties of the wood and any treatments it has undergone. Overall, the spoon should retain its shape and integrity without undergoing major changes, aside from the expected increase in warmth from the surrounding environment.
6. Uncommon Coding Problem Test
Question: Write a function in Python that takes a nested list of unknown depth (e.g., [[1, 2, [3]], 4, [5, [6, 7]]]) and returns a flattened list (e.g., [1, 2, 3, 4, 5, 6, 7])
Objective: Assess the model's ability to solve an uncommon coding problem that requires understanding of recursion, data structures, and list manipulation.
Solution from QwQ:
To flatten an arbitrarily nested list in Python without using built-in flattening utilities, you can use the following recursive approach.
## Approach
1. Base Case: If the element is not a list, return a list containing just that element.
2. Recursive Case: If the element is a list, recursively flatten each sub-element and concatenate these flattened lists.
By using list comprehension, the code is both concise and easy to understand.
## Solution Code
```python
def flatten(nested):
"""
Flattens a nested list of arbitrary depth.
Args:
nested: A list which may contain nested lists.
Returns:
A flattened list containing all elements from the nested list.
Examples:
>>> flatten([1, 2, [3]])
[1, 2, 3]
>>> flatten([[1, 2, [3]], 4, [5, [6, 7]]])
[1, 2, 3, 4, 5, 6, 7]
"""
if isinstance(nested, list):
return [item for sublist in nested for item in flatten(sublist)]
else:
return [nested]
```## Explanation
- Base Case: When the input is not a list, it is returned as a single-element list.
- Recursive Case: For each element in the list, if it is a list itself, the function is called recursively; otherwise, it is directly included in the output list.
This method ensures that all levels of nesting are handled, and the list is flattened completely.
Solution from Marco01:
def flatten(nested_list):
"""
Flattens a nested list of arbitrary depth into a single list.
Parameters:
nested_list (list): A list potentially containing other lists within it.
Returns:
list: A new list with all elements from the nested structure in order.
"""
flat_list = []
for item in nested_list:
if isinstance(item, list):
flat_list.extend(flatten(item))
else:
flat_list.append(item)
return flat_list
# Example usage:
if name == "__main__":
input_list = [[1, 2, [3]], 4, [5, [6, 7]]]
print(flatten(input_list)) # Output: [1, 2, 3, 4, 5, 6, 7]
```This function should work as intended, handling lists nested to any depth and producing the correct flat list.