instructions: How much has the City of Los Angeles spent fighting dust pollution from Owens Lake?
  
  Once a lake bed is exposed, winds kick up ferocious dust storms. Those windblown sediments contribute to air pollution and can contribute to asthma, lung cancer and cardiopulmonary disease, among other health issues. Owens Lake has been among the largest sources of dust pollution in the nation. The City of Los Angeles has spent more than $2.5 billion mitigating the dust through projects at the lake bed such as shallow flooding, seeding and planting vegetation, spreading gravel or tilling the ground.
candidate_responses: The City of Los Angeles has spent more than $2.5 billion mitigating the dust pollution from Owens Lake.
eval_prompts: Given an instruction and two responses—one generated by a human and the other by a language model—I'm seeking to evaluate how closely the language model's response mirrors the human-generated one. Additionally, I want to assess the accuracy and relevance of the language model's response to the original instruction.
  
  Instruction:
  ```
  How much has the City of Los Angeles spent fighting dust pollution from Owens Lake?
  
  Once a lake bed is exposed, winds kick up ferocious dust storms. Those windblown sediments contribute to air pollution and can contribute to asthma, lung cancer and cardiopulmonary disease, among other health issues. Owens Lake has been among the largest sources of dust pollution in the nation. The City of Los Angeles has spent more than $2.5 billion mitigating the dust through projects at the lake bed such as shallow flooding, seeding and planting vegetation, spreading gravel or tilling the ground.
  ```
  
  Human Response:
  ```
  The City of Los Angeles has spent more than $2.5 billion fighting dust pollution from Owens Lake.
  ```
  
  Language Model Response:
  ```
  The City of Los Angeles has spent more than $2.5 billion mitigating the dust pollution from Owens Lake.
  ```
  
  You are quality assessor who analyzes the similarity between the Human Response and the Language Model Response on a scale of 1 to 100, where 1 indicates no similarity and 100 indicates identical responses.
  Also you analyze the Language Model Response how it accurately answers the given Instruction on a scale of 1 to 100. Analysis MUST be rigorous and thorough.
  Provide the assessment in the following JSON format:
  
  {
    "similarity_assessment": {"score": [Insert similarity score here],"reason": [Insert how the similarity score is determined]},
    "precision_assessment": {"score": [Insert precision score here],"reason": [Insert how the precision score is determined]}
  }
  