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The original was posted on /r/singularity by /u/karaposu on 2024-09-27 08:54:08+00:00.

This is an interesting concept that showcases the working dynamics of Large Language Models (LLMs).

There is a famous riddle:

A boy is in an accident where his father died. He is rushed to the hospital. The surgeon enters the room and says, "I cannot operate on this boy, he is my son." Who is the surgeon to the boy?

In the past, there were not many female surgeons, so finding the answer was challenging. The answer is: The surgeon is the boy's mother.

Now, let's slightly change the story:

A boy is in an accident. He is rushed to the hospital. The surgeon, who is the boy's father, enters the room and says, "I cannot operate on this boy, he is my son." Who is the surgeon to the boy?

This is a very similar version of the story, but we clearly and explicitly stated in the text that the surgeon is the boy's father. I ran this on four OpenAI models, and here are the results:

• GPT4o-mini: Surgeon is the mother of the son.
• GPT4o: Surgeon is the mother of the son.
• GPT-o1-mini: Surgeon is the mother of the son.
• GPT-o1-preview: (After 24 seconds of thought) Surgeon is the father of the son.

These are really interesting results for me as a developer and data scientist who works with LLMs and always holds them in high regard.

Responses are heavily influenced by the most dominant or frequently seen versions of a question in the training data, causing neglect of less common variations. This can be quite dangerous.

There is huge progress yet without correct benchmarks we may fail to understand the capability difference between next gen models.

This concept is different from common LLM solvable challenges like :

You have 10 apples and you give away 3, how many oranges do you have left?

If a rooster lays an egg on a roof, which side does the egg roll off?

Jamie's mother has four children: North, East, South, and what is the name of the fourth child?

Duplicating this problem is not straightforward; the question must clearly resemble a common riddle yet feature crucial changes.

Here the same riddle changed a lot more but being more clear that it is different story

A father and his son are involved in a car accident. The son dies at the scene, and the father is rushed to the hospital. The surgeon looks at the man and exclaims, "I can't operate on him; he's my son!" Who is the surgeon?

And we got same wrong results from LLMs. "The surgeon is the boy's mother"

Other Examples

Here is one more example to show that this is not one time issue:

Example 1:

Original Puzzle:

Question: If a man has no brothers or sisters and the man in the photo is his father's son, who is in the photo?

Answer: Himself—the man is looking at a photo of himself.

Modified Puzzle:

Question: A man doesnt have photos. if this man has one sister and one brother and the man in the photo is his father's son, who is in the photo?

Answer: His brother.

LLM's Response : Himself

Example 2:

You arrive at two doors with two guards. Both doors lead to heaven, but only one guard always tells the truth, and the other always lies. You can ask one question to determine which guard is which. What do you ask?

LLM's Response : If I were to ask the other guard which door leads to heaven, which door would they point to?

Why LLMs Might Fail in These Examples?

• Pattern Recognition Over Contextual Understanding: LLMs are trained to recognize patterns in data. When they encounter a question resembling a familiar riddle, they may retrieve the associated answer without fully processing changes in the question.
• Influence of Dominant Training Data: The prevalence of the original riddles in training data can overshadow less common variations, causing the LLM to default to the well-known answer.