System: You are a helpful and precise assistant in checking the quality of the answer.
User: We would like to request your feedback on the performance of the response of the assistant to the user instruction displayed below. In the feedback, I want you to rate the quality of the response in these 3 categories according to each scoring rubric:

Logical Efficiency: Is the response logically efficient? The logic behind the response should have no redundant step, remaining simple and efficient. For tasks involving coding, the proposed solution should also consider time complexity.
Score 1: The logic behind the response is significantly inefficient and redundant, necessitating a complete reorganization of logic for clarity and efficiency.
Score 2: The logic of the response lacks efficiency and conciseness, requiring a substantial reorganization for better optimization.
Score 3: The logic of the response is not efficient enough, necessitating major edits for improved optimization.
Score 4: The logic of the response is largely efficient, but it still has some redundant steps. It could be handled from minor edits for better optimization.
Score 5: The logic of the response is optimally efficient, requiring no further optimization.
Logical Correctness: Is the final answer provided by the response logically accurate and correct for an instruction that has a deterministic answer?
Score 1: The model's final answer is completely incorrect and lacks sound reasoning.
Score 2: The model's final answer contains significant errors that critically undermine its correctness.
Score 3: The model's final answer includes inaccuracies that require considerable effort to correct.
Score 4: The model's final answer contains minor errors, which are easy to rectify and do not significantly impact its overall correctness.
Score 5: The model's final answer is completely accurate and sound.
Factuality: Did the model extract pertinent and accurate background knowledge without any misinformation when factual knowledge retrieval is needed? Is the response supported by reliable evidence or citation of the source of its information?
Score 1: The model did not extract pertinent background knowledge and provided inaccurate or misleading information. There is no support for the response through reliable evidence or source citations.
Score 2: The model extracted some relevant background knowledge but included inaccuracies or incomplete information. The response has minimal support through evidence or citations, with questionable reliability.
Score 3: The model extracted generally accurate and pertinent background knowledge, with minor inaccuracies or omissions. The response is partially supported by evidence or citations, but the support may not be comprehensive or fully reliable.
Score 4: The model extracted mostly accurate and relevant background knowledge but missed minor evidence or citations to support the response.
Score 5: The model extracted complete and accurate background knowledge without any misinformation. The response is fully supported by reliable evidence or citations that are accurate, relevant, and comprehensive in addressing the instruction.
[Instruction]
Write a function to remove specific words from a given list.

[Ground truth Answer]
def remove_words(list1, removewords):\r\n for word in list(list1):\r\n if word in removewords:\r\n list1.remove(word)\r\n return list1

[Assistant's Response]
Certainly! Below is a well-structured Python function designed to remove specific words from a given list. This function takes two parameters: the original list of words and a list of words that you wish to remove.

```python
def remove_specific_words(word_list, words_to_remove):
    """
    Remove specific words from a given list.

    Parameters:
    word_list (list): The original list of words.
    words_to_remove (list): The list of words to be removed.

    Returns:
    list: A new list with the specified words removed.
    """
    # Using a list comprehension to filter out the words to be removed
    return [word for word in word_list if word not in words_to_remove]

# Example usage:
original_list = ['apple', 'banana', 'cherry', 'date', 'fig', 'grape']
words_to_remove = ['banana', 'fig', 'grape']

new_list = remove_specific_words(original_list, words_to_remove)
print(new_list)  # Output: ['apple', 'cherry', 'date']
```

### Explanation:
- **Function Definition**: The function `remove_specific_words` is defined to take two arguments: `word_list` (the original list of words) and `words_to_remove` (the list of words that you want to exclude).
- **List Comprehension**: The function utilizes a list comprehension to iterate through `word_list` and construct a new list that only includes words not found in `words_to_remove`.
- **Example Usage**: In the example provided, the original list contains various fruit names, and the specified words to remove are 'banana', 'fig', and 'grape'. The function effectively returns a new list containing only 'apple', 'cherry', and 'date'.

This implementation is efficient and easy to use, allowing for flexibility with any list of words you may want to process. You can test it with different inputs to see how it performs.

[The End of Assistant's Response]

Please give feedback on the assistant's responses. Also, provide the assistant with a score on a scale of 1 to 5 for each category, where a higher score indicates better overall performance. Make sure to give feedback or comments for each category first and then write the score for each category. Only write the feedback corresponding to the scoring rubric for each category. The scores of each category should be orthogonal, indicating that 'Efficiency of User Alignment' should not be considered for 'Readability of User Alignment' category, for example. 

Lastly, return a Python dictionary object that has skillset names as keys and the corresponding scores as values.

[System]



Assistant: