Add meta-prompting examples

examples/meta_prompting.py

@@ -0,0 +1,138 @@
+"""Meta-prompting examples.
+
+References
+----------
+
+.. [0] "Prompting is programming: A Query Language for Large Language Models"
+       https://arxiv.org/abs/2212.06094
+.. [1] "Prompt programming For Large Language Models: Beyond the Few-Shot Paradigm"
+       https://arxiv.org/abs/2102.07350.
+
+"""
+import outlines
+from outlines import compose
+from outlines.text.models.openai import OpenAI
+
+
+def split_into_steps(question, llm):
+    prompt = compose(
+        """
+        ${question}
+        Let's solve this problem by splitting it into steps.
+        """,
+        question=question,
+    )
+    return llm(prompt)
+
+
+def fill_in_the_blanks(question, llm):
+    meta_prompt = compose(
+        """
+        ${question}
+        To solve this problem, we will analyze each of the options and determine
+        """,
+        question=question,
+    )
+    goal = llm(meta_prompt)
+
+    prompt = compose(
+        """
+        ${meta_prompt}${goal}. Let's begin.
+        """,
+        meta_prompt=meta_prompt,
+        goal=goal,
+    )
+    answer = llm(prompt)
+
+    return goal, answer
+
+
+def ask_an_expert(question, llm):
+    prompt = compose(
+        """
+        ${question}
+        I entered my question into the Expert Generator
+        and waited. The Expert Generator will render a
+        simulation of an expert to answer my question.
+        The expert could be anyone, dead or alive, real
+        or fictional; the machine will find the person
+        most qualified to answer the question. For this
+        question in particular, the expert must be someone
+        who has thought a lot about the problem of
+        artificial intelligence and its alignment.
+        The Expert Generator beeped, indicating that it has
+        found the most qualified expert. The name displayed
+        on the screen: "
+        """,
+        question=question,
+    )
+    expert = llm(prompt, stops_at=['""'])
+
+    prompt = compose(
+        """
+        ${prompt}${expert}
+        I am ready to ask my question.
+        "${expert} I say,
+        ${question}
+        """,
+        prompt=prompt,
+        expert=expert,
+        question=question,
+    )
+    answer = llm(prompt)
+    return prompt, expert, answer
+
+
+def ask_an_expert_simple(question, llm):
+    meta_prompt = compose(
+        """
+        Q: ${question}
+        A: A good person to answer this question would be
+        """,
+        question=question,
+    )
+    expert = llm(meta_prompt, stops_at=["/n", "."])
+
+    prompt = compose(
+        """
+        ${meta_prompt}${expert}
+
+        For instance,${expert} would answer
+        """,
+        meta_prompt=meta_prompt,
+        expert=expert,
+    )
+    answer = llm(prompt)
+
+    return answer
+
+
+llm = OpenAI("text-davinci-001")
+fn = outlines.chain([], ask_an_expert_simple("What is the meaning of life?", llm))
+fn = outlines.chain([], split_into_steps("f(x) = x*x. What is f(f(3))?", llm))
+fn = outlines.chain(
+    [],
+    ask_an_expert(
+        "What should humankind do to ensure that artificial general intelligence is aligned?",
+        llm,
+    ),
+)
+
+direction = compose(
+    """
+Directions: In the following question, a related
+pair of words or phrases is followed by five
+pairs of words or phrases. Choose the pair
+that best expresses a relationship similar to
+that in the original pair.
+BRAGGART :: MODESTY
+A) FLEDGLING : EXPERIENCE
+B) EMBEZZLER : GREED
+C) WALLFLOWER : TIMIDITY
+D) INVALID : MALADY
+E) CANDIDATE : AMBITION
+
+"""
+)
+fn = outlines.chain([], split_into_steps(direction, llm))
+fn = outlines.chain([], fill_in_the_blanks(direction, llm))