Response A states "there are three dogs", which is incorrect according to the reference answer. \n\nIn terms of helpfulness, Response B gives the precise number while Response A provides an inaccurate guess. For relevance, both refer to dogs in the park from the question. However, Response B is more correct and factual based on the reference answer. Response A shows some attempt at reasoning but is ultimately incorrect. Response B requires less depth of thought to simply state the factual number.\n\nIn summary, Response B is superior in terms of helpfulness, relevance, correctness, and depth. My final decision is: [[B]]\n',     'value': 'B',     'score': 0}Customize the Evaluation Prompt‚ÄãYou can use your own custom evaluation prompt to add more task-specific instructions or to instruct the evaluator to score the output.*Note: If you use a prompt that expects generates a result in a unique format, you may also have to pass in a custom output parser (output_parser=your_parser()) instead of the default PairwiseStringResultOutputParserfrom langchain.prompts import PromptTemplateprompt_template = PromptTemplate.from_template(    """Given the input context, which do you prefer: A or B?Evaluate based on the following criteria:{criteria}Reason step by step and finally, respond with either [[A]] or [[B]] on its own line.DATA----input: {input}reference: {reference}A: {prediction}B: {prediction_b}---Reasoning:""")evaluator = load_evaluator(    "labeled_pairwise_string", prompt=prompt_template)# The prompt was assigned to the evaluatorprint(evaluator.prompt)    input_variables=['prediction', 'reference', 'prediction_b', 'input'] output_parser=None partial_variables={'criteria': 'helpfulness: Is the submission helpful, insightful, and appropriate?\nrelevance: Is the submission referring to a real quote from the text?\ncorrectness: Is the submission correct, accurate, and factual?\ndepth: Does the submission demonstrate depth of thought?'} template='Given the input context,
Open In Colab
Open In Colab ->: Response A states "there are three dogs", which is incorrect according to the reference answer. \n\nIn terms of helpfulness, Response B gives the precise number while Response A provides an inaccurate guess. For relevance, both refer to dogs in the park from the question. However, Response B is more correct and factual based on the reference answer. Response A shows some attempt at reasoning but is ultimately incorrect. Response B requires less depth of thought to simply state the factual number.\n\nIn summary, Response B is superior in terms of helpfulness, relevance, correctness, and depth. My final decision is: [[B]]\n',     'value': 'B',     'score': 0}Customize the Evaluation Prompt‚ÄãYou can use your own custom evaluation prompt to add more task-specific instructions or to instruct the evaluator to score the output.*Note: If you use a prompt that expects generates a result in a unique format, you may also have to pass in a custom output parser (output_parser=your_parser()) instead of the default PairwiseStringResultOutputParserfrom langchain.prompts import PromptTemplateprompt_template = PromptTemplate.from_template(    """Given the input context, which do you prefer: A or B?Evaluate based on the following criteria:{criteria}Reason step by step and finally, respond with either [[A]] or [[B]] on its own line.DATA----input: {input}reference: {reference}A: {prediction}B: {prediction_b}---Reasoning:""")evaluator = load_evaluator(    "labeled_pairwise_string", prompt=prompt_template)# The prompt was assigned to the evaluatorprint(evaluator.prompt)    input_variables=['prediction', 'reference', 'prediction_b', 'input'] output_parser=None partial_variables={'criteria': 'helpfulness: Is the submission helpful, insightful, and appropriate?\nrelevance: Is the submission referring to a real quote from the text?\ncorrectness: Is the submission correct, accurate, and factual?\ndepth: Does the submission demonstrate depth of thought?'} template='Given the input context,