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": ["🐠", "🐟", "ðŸĄ", "🐎", "ðŸģ"],
"Toy Story": ["🚀", "⚔ïļ,", "ðŸĪ–", "ðŸ‘―", "🌌"]}
. 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

1

you might might answer
1
2
{"Finding Nemo": ["🐠", "🐟", "ðŸĄ", "🐎", "ðŸģ"],
"Toy Story": ["🚀", "⚔ïļ,", "ðŸĪ–", "ðŸ‘―", "🌌"]}
. 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": ["ðŸĶ", "👑", "🌅", "ðŸū", "ðŸŽķ"],
  "The Nightmare Before Christmas": ["🎃", "ðŸ‘ŧ", "🎄", "🌙", "💀"],
  "The Godfather": ["ðŸ‘Ī", "ðŸ”Ŧ", "🍝", "ðŸ’ĩ", "🚎"],
  "The Matrix": ["💊", "ðŸ•ķ", "ðŸ”ĩ", "ðŸ”ī", "ðŸ’ŧ"],
  "Casablanca": ["🏙", "â™Ĩ", "☕", "ðŸŽķ", "🌙"],
  "Raiders of the Lost Ark": ["ðŸ‘Ļ", "🗚", "🏚", "🐍", "ðŸ”Ķ"],
  "V for Vendetta": ["🎭", "ðŸ’Ģ", "🚎", "ðŸ”Ĩ", "ðŸ—ģ"],
  "The Princess Bride": ["ðŸ‘ļ", "ðŸĪī", "⚔", "â™Ĩ", "😂"],
  "Back to the Future": ["⏰", "🚗", "⚡", "ðŸ’Ą", "🕰"],
  "Dune": ["🏜", "ðŸŠą", "⚔", "ðŸ’Ŧ", "🌌"]
}

Extract Json

I extracted the following JSON from that output:

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{
  "The Lion King": ["ðŸĶ", "👑", "🌅", "ðŸū", "ðŸŽķ"],
  "The Nightmare Before Christmas": ["🎃", "ðŸ‘ŧ", "🎄", "🌙", "💀"],
  "The Godfather": ["ðŸ‘Ī", "ðŸ”Ŧ", "🍝", "ðŸ’ĩ", "🚎"],
  "The Matrix": ["💊", "ðŸ•ķ", "ðŸ”ĩ", "ðŸ”ī", "ðŸ’ŧ"],
  "Casablanca": ["🏙", "â™Ĩ", "☕", "ðŸŽķ", "🌙"],
  "Raiders of the Lost Ark": ["ðŸ‘Ļ", "🗚", "🏚", "🐍", "ðŸ”Ķ"],
  "V for Vendetta": ["🎭", "ðŸ’Ģ", "🚎", "ðŸ”Ĩ", "ðŸ—ģ"],
  "The Princess Bride": ["ðŸ‘ļ", "ðŸĪī", "⚔", "â™Ĩ", "😂"],
  "Back to the Future": ["⏰", "🚗", "⚡", "ðŸ’Ą", "🕰"],
  "Dune": ["🏜", "ðŸŠą", "⚔", "ðŸ’Ŧ", "🌌"]
}

PyFunc

Resulting in output: [['ðŸĶ', '👑', '🌅', 'ðŸū', 'ðŸŽķ'], ['🎃', 'ðŸ‘ŧ', '🎄', '🌙', '💀'], ['ðŸ‘Ī', 'ðŸ”Ŧ', '🍝', 'ðŸ’ĩ', '🚎'], ['💊', 'ðŸ•ķ', 'ðŸ”ĩ', 'ðŸ”ī', 'ðŸ’ŧ'], ['🏙', 'â™Ĩ', '☕', 'ðŸŽķ', '🌙'], ['ðŸ‘Ļ', '🗚', '🏚', '🐍', 'ðŸ”Ķ'], ['🎭', 'ðŸ’Ģ', '🚎', 'ðŸ”Ĩ', 'ðŸ—ģ'], ['ðŸ‘ļ', 'ðŸĪī', '⚔', 'â™Ĩ', '😂'], ['⏰', '🚗', '⚡', 'ðŸ’Ą', '🕰'], ['🏜', 'ðŸŠą', '⚔', 'ðŸ’Ŧ', '🌌']]

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

1

You might answer
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["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies:
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['🎃', 'ðŸ‘ŧ', '🎄', '🌙', '💀'],
['ðŸ‘Ī', 'ðŸ”Ŧ', '🍝', 'ðŸ’ĩ', '🚎'],
['💊', 'ðŸ•ķ', 'ðŸ”ĩ', 'ðŸ”ī', 'ðŸ’ŧ'],
['🏙', 'â™Ĩ', '☕', 'ðŸŽķ', '🌙'],
['ðŸ‘Ļ', '🗚', '🏚', '🐍', 'ðŸ”Ķ'],
['🎭', 'ðŸ’Ģ', '🚎', 'ðŸ”Ĩ', 'ðŸ—ģ'],
['ðŸ‘ļ', 'ðŸĪī', '⚔', 'â™Ĩ', '😂'],
['⏰', '🚗', '⚡', 'ðŸ’Ą', '🕰'],
['🏜', 'ðŸŠą', '⚔', 'ðŸ’Ŧ', '🌌']]
What are the names of the movie titles?

Output

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "La La Land",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output:

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "La La Land",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True

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

1

you might might answer
1
2
{"Finding Nemo": ["🐠", "🐟", "ðŸĄ", "🐎", "ðŸģ"],
"Toy Story": ["🚀", "⚔ïļ,", "ðŸĪ–", "ðŸ‘―", "🌌"]}
. 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.

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

1

you might might answer
1
2
{"Finding Nemo": ["🐠", "🐟", "ðŸĄ", "🐎", "ðŸģ"],
"Toy Story": ["🚀", "⚔ïļ,", "ðŸĪ–", "ðŸ‘―", "🌌"]}
. 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": ["ðŸĶ", "👑", "🌅", "ðŸŒŋ"],
  "The Nightmare Before Christmas": ["🎃", "ðŸ‘ŧ", "💀", "ðŸ•ļ", "🕷"],
  "The Godfather": ["ðŸ”Ŧ", "💞", "🍝", "👔"],
  "The Matrix": ["💊", "ðŸ”ĩ", "ðŸ•ķ", "ðŸ’ŧ"],
  "Casablanca": ["✈", "âĪïļ", "☕", "🌃"],
  "Raiders of the Lost Ark": ["🗚", "🏚", "ðŸ‘Ģ", "ðŸ”Ķ", "🏜"],
  "V for Vendetta": ["🎭", "ðŸ”Ĩ", "ðŸ’Ģ", "ðŸ—Ą"],
  "The Princess Bride": ["ðŸĪš", "âĪïļ", "⚔", "👑", "🏰"],
  "Back to the Future": ["⌚", "⚡", "🚗", "âģ"],
  "Dune": ["🏜", "🐛", "⚔", "🌌"]
}

Extract Json

I extracted the following JSON from that output:

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{
  "The Lion King": ["ðŸĶ", "👑", "🌅", "ðŸŒŋ"],
  "The Nightmare Before Christmas": ["🎃", "ðŸ‘ŧ", "💀", "ðŸ•ļ", "🕷"],
  "The Godfather": ["ðŸ”Ŧ", "💞", "🍝", "👔"],
  "The Matrix": ["💊", "ðŸ”ĩ", "ðŸ•ķ", "ðŸ’ŧ"],
  "Casablanca": ["✈", "âĪïļ", "☕", "🌃"],
  "Raiders of the Lost Ark": ["🗚", "🏚", "ðŸ‘Ģ", "ðŸ”Ķ", "🏜"],
  "V for Vendetta": ["🎭", "ðŸ”Ĩ", "ðŸ’Ģ", "ðŸ—Ą"],
  "The Princess Bride": ["ðŸĪš", "âĪïļ", "⚔", "👑", "🏰"],
  "Back to the Future": ["⌚", "⚡", "🚗", "âģ"],
  "Dune": ["🏜", "🐛", "⚔", "🌌"]
}

PyFunc

Resulting in output: [['ðŸĶ', '👑', '🌅', 'ðŸŒŋ'], ['🎃', 'ðŸ‘ŧ', '💀', 'ðŸ•ļ', '🕷'], ['ðŸ”Ŧ', '💞', '🍝', '👔'], ['💊', 'ðŸ”ĩ', 'ðŸ•ķ', 'ðŸ’ŧ'], ['✈', 'âĪïļ', '☕', '🌃'], ['🗚', '🏚', 'ðŸ‘Ģ', 'ðŸ”Ķ', '🏜'], ['🎭', 'ðŸ”Ĩ', 'ðŸ’Ģ', 'ðŸ—Ą'], ['ðŸĪš', 'âĪïļ', '⚔', '👑', '🏰'], ['⌚', '⚡', '🚗', 'âģ'], ['🏜', '🐛', '⚔', '🌌']]

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

1

You might answer
1
["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies:
1
2
3
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5
6
7
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9
['🎃', 'ðŸ‘ŧ', '💀', 'ðŸ•ļ', '🕷'],
['ðŸ”Ŧ', '💞', '🍝', '👔'],
['💊', 'ðŸ”ĩ', 'ðŸ•ķ', 'ðŸ’ŧ'],
['✈', 'âĪïļ', '☕', '🌃'],
['🗚', '🏚', 'ðŸ‘Ģ', 'ðŸ”Ķ', '🏜'],
['🎭', 'ðŸ”Ĩ', 'ðŸ’Ģ', 'ðŸ—Ą'],
['ðŸĪš', 'âĪïļ', '⚔', '👑', '🏰'],
['⌚', '⚡', '🚗', 'âģ'],
['🏜', '🐛', '⚔', '🌌']]
What are the names of the movie titles?

Output

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[
  "The Lion King",
  "Halloween",
  "The Godfather",
  "The Matrix",
  "Up in the Air",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output:

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[
  "The Lion King",
  "Halloween",
  "The Godfather",
  "The Matrix",
  "Up in the Air",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True