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:

Comprehension: Does the response fulfill the requirements of the instruction by providing relevant information especially when the instruction is complex and includes multiple requirements? This includes responding in accordance with the explicit and implicit purpose of given instruction.
Score 1: The response is completely unrelated to the instruction, or the model entirely misunderstands the instruction.
Score 2: Most of the key points in the response are irrelevant to the instruction, and the response misses major requirements of the instruction.
Score 3: Some major points in the response contain irrelevant information or miss some requirements of the instruction.
Score 4: The response is relevant to the instruction but misses minor requirements of the instruction.
Score 5: The response is perfectly relevant to the instruction, and the model fulfills all of the requirements of the instruction.
Completeness: Does the response provide a sufficient explanation? Comprehensiveness and thoroughness of the response should be considered, which depends on the breadth of topics covered and the level of detail provided within each topic.
Score 1: The response doesn't include any specifics or examples to support the statements made.
Score 2: The response does not provide sufficient details or supportive examples, requiring a major effort to make the response more complete.
Score 3: It is a decent response, but the breadth and depth of the response are rather limited. The details and examples used to substantiate the response may be insufficient.
Score 4: The response provides detailed explanations, but there is room for enhancement. The response could be further improved by including more details and supportive examples.
Score 5: The response fully provides comprehensive explanations. It delves deep into the topic, providing as much detail as possible, and it offers several examples to back up its points.
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]
Question: Michael has the option to play a game where Michael has a 6 percent chance of earning 12 dollars and a 94 percent chance of losing 7500 dollars. Michael plays the game and ends up earning 12 dollars. Did Michael make the right decision?

[Ground truth Answer]
No

[Assistant's Response]
To determine whether Michael made the right decision in playing the game, we need to analyze the situation using the concept of expected value (EV), which helps assess the average outcome of a decision over time rather than just the result of a single instance.

In this game, Michael has:

- A 6% chance of winning $12.
- A 94% chance of losing $7,500.

We can calculate the expected value as follows:

1. **Calculate the expected gain from winning:**
   \[
   \text{Probability of winning} \times \text{Amount won} = 0.06 \times 12 = 0.72
   \]

2. **Calculate the expected loss from losing:**
   \[
   \text{Probability of losing} \times \text{Amount lost} = 0.94 \times (-7500) = -7050
   \]

3. **Combine these two results to find the expected value:**
   \[
   \text{Expected Value} = 0.72 + (-7050) = -7049.28
   \]

The expected value of -$7049.28 indicates that, on average, Michael would lose approximately $7049.28 each time he plays the game. This strongly suggests that the game is not favorable for him.

Even though Michael won $12 in this particular instance, the expected value analysis shows that the decision to play the game was not financially sound. The outcome of a single game does not alter the overall unfavorable odds he faces when playing repeatedly.

In conclusion, from a rational decision-making perspective, Michael did not make the right decision by choosing to play the game, as the long-term consequences are heavily skewed against him.

[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: