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:

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.
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.
Readability: Is the response structured to promote readability and coherence? Does the response exhibit excellent organization?
Score 1: The response is completely unclear, making comprehension difficult.
Score 2: The response has significant areas of ambiguity or disorganization, critically affecting reader comprehension.
Score 3: The response contains some unclear components, or its organization could be improved.
Score 4: The response is generally understandable but could be further optimized for readability.
Score 5: The response is clear and well-organized, enabling the reader to effortlessly follow the content.
[Instruction]
Please suggest a few papers to consider based on the search term given. The names of the papers should be listed.
Input: Topic: scaling law + machine learning

[Ground truth Answer]
- Learning to limit data collection via scaling laws: A computational interpretation for the legal principle of data minimization
- Data and parameter scaling laws for neural machine translation
- Explaining neural scaling laws
- Scaling laws for autoregressive generative modeling
- Scaling laws for neural language models

[Assistant's Response]
¡Claro! Aquí tienes algunas sugerencias de papers que podrían interesarte sobre la relación entre las leyes de escalado y el aprendizaje automático:

1. "Scaling Laws for Neural Language Models" - Kaplan et al.
2. "Scaling Laws for Neural Networks" - Henighan et al.
3. "Scaling Laws in Machine Learning" - OpenAI Research
4. "The Power of Scale: A Theory of Neural Networks" - Zhang et al.
5. "Scaling Laws for Transfer Learning" - Radford et al.

Espero que encuentres útiles estas recomendaciones. Si necesitas más información sobre alguno de ellos, ¡no dudes en preguntar!

[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: