Why do LLMs sometimes fail at seemingly simple tasks?

LLMs can solve advanced tasks yet fail at apparently simple ones because success on familiar language patterns does not guarantee consistent generalization to every wording, direction of a relationship, or rule. A simple-looking question may be unusual relative to the patterns the model learned.

1. The reversal curse: knowing A → B does not guarantee B → A

Suppose a model was trained on this made-up statement:

"Nora Vale wrote The Silver Orchard."

It might correctly answer "Who wrote The Silver Orchard?" yet fail another direction of a learned relation, depending on which relation was emphasized during training.

Research on the reversal curse found that some autoregressive models trained on "A is B" did not reliably infer "B is A", even when the relationship should be reversible. This is a finding about specific experimental conditions, not proof that every modern LLM fails every reverse question. Providing the statement in the current prompt can change the result.

2. Superficial changes can change the answer

These prompts should usually test the same knowledge:

• "Which option is correct? A. ... B. ..."
• "Choose the right response: B. ... A. ..."

Yet models can be sensitive to answer-option position, punctuation, formatting, and phrasing. In controlled studies, changing the order of multiple-choice options sometimes significantly changed performance.

Why? Learned positional and format preferences can interfere with applying the underlying task consistently. This problem is often called prompt brittleness.

3. The model may match a pattern instead of applying a general rule

A model may complete a familiar numerical or word pattern correctly but misapply it to unfamiliar symbols, longer sequences, or a slightly changed rule.

Example:

Rule: Every red ticket is valid.
Fact: Ticket X is red.
Question: Is X valid?
Correct: Yes.

A model that answers this correctly once may still fail when labels or irrelevant details are changed. One success is not enough to establish reliable rule use.

4. Multi-step dependence makes small tasks fragile

Even a task that looks short may require several decisions: interpret a condition, keep intermediate state, handle an exception, and format a final response. Errors in any step can change the answer.

For a workflow with five required decisions, if each independently succeeds 90% of the time, a toy calculation gives:

P(all five correct) = 0.9 ^ 5 ≈ 0.5905 = 59.0%

This is illustrative only: real LLM error events are not generally independent, and the assumption does not establish a real model's task-success rate.

5. Plausible text is not built-in verification

A model may produce an answer that is fluent and stylistically appropriate without executing code, checking a mathematical relation, or verifying a factual claim. Low-temperature or deterministic generation can make results more repeatable but cannot guarantee they are correct.

Practical way to identify these failures

Instead of testing a single prompt, build a small robustness suite around the same underlying task:

1. Paraphrase: Ask the identical question using several equivalent wordings.
2. Reverse relationships: Test both directions where the relation logically permits it.
3. Shuffle answer options: Reorder choices while keeping the correct answer unchanged.
4. Rename entities: Replace familiar names with made-up ones so memorization is less useful.
5. Add irrelevant details: See whether distracting context causes errors.
6. Check exact outputs: For arithmetic or code, compare results with a calculator or executable tests.

For example, if a model gets 9 of 10 original questions right but only 6 of 10 meaning-preserving variants right:

Original accuracy = 90%
Variant accuracy  = 60%
Observed gap      = 30 percentage points

That gap reveals a robustness concern on this particular sample, but does not by itself identify the cause. Use more representative cases and repeat trials before drawing broader conclusions.

What can improve reliability?

• Use clear, unambiguous prompts and consistent structured formats.
• Include varied paraphrases and reversed relations in evaluation or suitable training data.
• Use deterministic tools for exact arithmetic, constraints, and programmatic tasks.
• Break complex dependencies into testable steps where that improves verification.
• Track the worst-performing variations, not only mean accuracy on a favored prompt.

In short: "Simple for a person" does not mean "reliably learned by an LLM." Robustness testing reveals where a model has learned a surface pattern rather than a rule that transfers consistently.

See "What are the main limitations of Large Language Models?" for the wider overview of operational and reliability limitations.

Research references: The Reversal Curse (ICLR 2024), Sensitivity to option order (NAACL 2024), and When Punctuation Matters (EMNLP 2025).