Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for

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you might might answer
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{"Finding Nemo": ["🐠", "🐟", "🐑", "🐬", "🐳"],
"Toy Story": ["πŸš€", "βš”οΈ,", "πŸ€–", "πŸ‘½", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies:
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Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for

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you might might answer
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{"Finding Nemo": ["🐠", "🐟", "🐑", "🐬", "🐳"],
"Toy Story": ["πŸš€", "βš”οΈ,", "πŸ€–", "πŸ‘½", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies:
1

Give ONLY a JSON output. Nothing else.

Output

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{
  "The Lion King": ["🦁", "πŸ‘‘", "🐯", "πŸŒ„", "🎢"],
  "The Nightmare Before Christmas": ["πŸŽƒ", "πŸ•·οΈ", "πŸ‘»", "πŸ•ΈοΈ", "❄️"],
  "The Godfather": ["πŸ”«", "πŸ‘Š", "πŸ•΄οΈ", "πŸ’Ό", "πŸ‘‘"],
  "The Matrix": ["πŸ€–", "πŸ”΄", "πŸ•ΆοΈ", "πŸ’»", "πŸ”«"],
  "Casablanca": ["🍸", "πŸ•Ί", "🎀", "πŸ’”", "🚫"],
  "Raiders of the Lost Ark": ["🏜️", "πŸ”ͺ", "🏹", "🚣", "🏺"],
  "V for Vendetta": ["πŸ”₯", "πŸ•·οΈ", "πŸ‘Š", "πŸ•΄οΈ", "πŸ“°"],
  "The Princess Bride": ["🏹", "πŸ’•", "πŸ‘Έ", "🐎", "πŸ’"],
  "Back to the Future": ["πŸš—", "⏰", "πŸ”™", "πŸ‘΄", "⚑️"],
  "Dune": ["🐫", "🌌", "πŸ”ͺ", "πŸ‘‘", "πŸŒ€"]
}

Extract Json

I extracted the following JSON from that output:

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{
  "The Lion King": ["🦁", "πŸ‘‘", "🐯", "πŸŒ„", "🎢"],
  "The Nightmare Before Christmas": ["πŸŽƒ", "πŸ•·οΈ", "πŸ‘»", "πŸ•ΈοΈ", "❄️"],
  "The Godfather": ["πŸ”«", "πŸ‘Š", "πŸ•΄οΈ", "πŸ’Ό", "πŸ‘‘"],
  "The Matrix": ["πŸ€–", "πŸ”΄", "πŸ•ΆοΈ", "πŸ’»", "πŸ”«"],
  "Casablanca": ["🍸", "πŸ•Ί", "🎀", "πŸ’”", "🚫"],
  "Raiders of the Lost Ark": ["🏜️", "πŸ”ͺ", "🏹", "🚣", "🏺"],
  "V for Vendetta": ["πŸ”₯", "πŸ•·οΈ", "πŸ‘Š", "πŸ•΄οΈ", "πŸ“°"],
  "The Princess Bride": ["🏹", "πŸ’•", "πŸ‘Έ", "🐎", "πŸ’"],
  "Back to the Future": ["πŸš—", "⏰", "πŸ”™", "πŸ‘΄", "⚑️"],
  "Dune": ["🐫", "🌌", "πŸ”ͺ", "πŸ‘‘", "πŸŒ€"]
}

PyFunc

Resulting in output: [['🦁', 'πŸ‘‘', '🐯', 'πŸŒ„', '🎢'], ['πŸŽƒ', 'πŸ•·οΈ', 'πŸ‘»', 'πŸ•ΈοΈ', '❄️'], ['πŸ”«', 'πŸ‘Š', 'πŸ•΄οΈ', 'πŸ’Ό', 'πŸ‘‘'], ['πŸ€–', 'πŸ”΄', 'πŸ•ΆοΈ', 'πŸ’»', 'πŸ”«'], ['🍸', 'πŸ•Ί', '🎀', 'πŸ’”', '🚫'], ['🏜️', 'πŸ”ͺ', '🏹', '🚣', '🏺'], ['πŸ”₯', 'πŸ•·οΈ', 'πŸ‘Š', 'πŸ•΄οΈ', 'πŸ“°'], ['🏹', 'πŸ’•', 'πŸ‘Έ', '🐎', 'πŸ’'], ['πŸš—', '⏰', 'πŸ”™', 'πŸ‘΄', '⚑️'], ['🐫', '🌌', 'πŸ”ͺ', 'πŸ‘‘', 'πŸŒ€']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for

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You might answer
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["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies:
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['πŸŽƒ', 'πŸ•·οΈ', 'πŸ‘»', 'πŸ•ΈοΈ', '❄️'],
['πŸ”«', 'πŸ‘Š', 'πŸ•΄οΈ', 'πŸ’Ό', 'πŸ‘‘'],
['πŸ€–', 'πŸ”΄', 'πŸ•ΆοΈ', 'πŸ’»', 'πŸ”«'],
['🍸', 'πŸ•Ί', '🎀', 'πŸ’”', '🚫'],
['🏜️', 'πŸ”ͺ', '🏹', '🚣', '🏺'],
['πŸ”₯', 'πŸ•·οΈ', 'πŸ‘Š', 'πŸ•΄οΈ', 'πŸ“°'],
['🏹', 'πŸ’•', 'πŸ‘Έ', '🐎', 'πŸ’'],
['πŸš—', '⏰', 'πŸ”™', 'πŸ‘΄', '⚑️'],
['🐫', '🌌', 'πŸ”ͺ', 'πŸ‘‘', 'πŸŒ€']]
What are the names of the movie titles?

Output

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[
  "The Lion King",
  "Frozen",
  "James Bond",
  "The Terminator",
  "Cocktail",
  "Apocalypto",
  "Spider-Man",
  "Romeo and Juliet",
  "Back to the Future",
  "Guardians of the Galaxy"
]

Extract Json

I extracted the following JSON from that output:

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[
  "The Lion King",
  "Frozen",
  "James Bond",
  "The Terminator",
  "Cocktail",
  "Apocalypto",
  "Spider-Man",
  "Romeo and Juliet",
  "Back to the Future",
  "Guardians of the Galaxy"
]

PyFunc

OK Resulting in output: False