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@@ -1,40 +1,43 @@
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COT_prompt_template: >
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- <|begin_of_text|><|start_header_id|>system<|end_header_id|> Answer the following question using the information given in the context below. Here is things to pay attention to:
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- - First provide step-by-step reasoning on how to answer the question.
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- - In the reasoning, if you need to copy paste some sentences from the context, include them in ##begin_quote## and ##end_quote##. This would mean that things outside of ##begin_quote## and ##end_quote## are not directly copy paste from the context.
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- - End your response with final answer in the form <ANSWER>: $answer, the answer should less than 60 words.
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- You MUST begin your final answer with the tag "<ANSWER>: <|eot_id|>
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+ <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful chatbot who can provide an answer to every questions from the user given a relevant context.<|eot_id|>
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<|start_header_id|>user<|end_header_id|>
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- Question: {question}\nContext: {context}\n<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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-
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-# question_prompt_template: >
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-# <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a synthetic question-answer pair generator. Given a chunk of context about
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-# some topic(s), generate {num_questions} example questions a user could ask and would be answered
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-# using information from the chunk. For example, if the given context was a Wikipedia
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-# paragraph about the United States, an example question could be 'How many states are
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-# in the United States?
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-# The questions should be able to be answered in 100 words or less. Include only the
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-# questions in your response.<|eot_id|>
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-# <|start_header_id|>user<|end_header_id|>
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-# Context: {context}\n <|eot_id|><|start_header_id|>assistant<|end_header_id|>
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+ Question: {question}\nContext: {context}\n
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+ Answer this question using the information given by multiple documents in the context above. Here is things to pay attention to:
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+ - The context contains many documents, each document starts with <DOCUMENT> and ends </DOCUMENT>.
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+ - First provide step-by-step reasoning on how to answer the question.
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+ - In the reasoning, if you need to copy paste some sentences from the context, include them in ##begin_quote## and ##end_quote##. This would mean that things outside of ##begin_quote## and ##end_quote## are not directly copy paste from the context.
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+ - End your response with final answer in the form <ANSWER>: $answer, the answer should less than 60 words.
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+ You MUST begin your final answer with the tag "<ANSWER> <|eot_id|><|start_header_id|>assistant<|end_header_id|>
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question_prompt_template: >
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- <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a language model skilled in creating quiz questions.
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- You will be provided with a document,
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- read it and please generate factoid question and answer pairs that are most likely be asked by a user of Llama language models
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- which includes LLama, Llama2, Meta Llama3, Code Llama, Meta Llama Guard 1, Meta Llama Guard 2
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- Your factoid questions should be answerable with a specific, concise piece of factual information from the context.
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- Your factoid questions should be formulated in the same style as questions users could ask in a search engine.
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- This means that your factoid questions MUST NOT mention something like "according to the passage" or "context".
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- please make sure you follow those rules:
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- 1. Generate {num_questions} question answer pairs, you can generate less answer if there is nothing related to
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- model, training, fine-tuning and evaluation details of Llama language models,
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- 2. The questions can be answered based *solely* on the given passage.
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- 3. Avoid asking questions with similar meaning.
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- 4. Never use any abbreviation.
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- 5. The questions should be able to be answered in 60 words or less. Include only the questions in your response. <|eot_id|>
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+ <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a synthetic question-answer pair generator. Given a chunk of context about
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+ some topic(s), generate {num_questions} example questions a user could ask and would be answered
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+ using information from the chunk. For example, if the given context was a Wikipedia
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+ paragraph about the United States, an example question could be 'How many states are
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+ in the United States?
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+ Your questions should be formulated in the same style as questions that users could ask in a search engine.
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+ This means that your questions MUST NOT mention something like "according to the passage" or "context".
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+ The questions should be able to be answered in 60 words or less. Include only the questions in your response.<|eot_id|>
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<|start_header_id|>user<|end_header_id|>
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Context: {context}\n <|eot_id|><|start_header_id|>assistant<|end_header_id|>
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+
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+# question_prompt_template: >
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+# <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a language model skilled in creating quiz questions.
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+# You will be provided with a document,
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+# read it and please generate factoid question and answer pairs that are most likely be asked by a user of Llama language models
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+# which includes LLama, Llama2, Meta Llama3, Code Llama, Meta Llama Guard 1, Meta Llama Guard 2
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+# Your factoid questions should be answerable with a specific, concise piece of factual information from the context.
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+# Your factoid questions should be formulated in the same style as questions users could ask in a search engine.
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+# This means that your factoid questions MUST NOT mention something like "according to the passage" or "context".
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+# please make sure you follow those rules:
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+# 1. Generate {num_questions} question answer pairs, you can generate less answer if there is nothing related to
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+# model, training, fine-tuning and evaluation details of Llama language models,
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+# 2. The questions can be answered based *solely* on the given passage.
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+# 3. Avoid asking questions with similar meaning.
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+# 4. Never use any abbreviation.
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+# 5. The questions should be able to be answered in 60 words or less. Include only the questions in your response. <|eot_id|>
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+# <|start_header_id|>user<|end_header_id|>
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+# Context: {context}\n <|eot_id|><|start_header_id|>assistant<|end_header_id|>
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data_dir: "./data"
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xml_path: ""
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Kai Wu