Why is sarcasm so hard for LLMs to detect?

Sarcasm inverts literal meaning using context, tone, and exaggeration rather than keywords. A model that reads only the words sees praise where a human sees mockery. The prompt teaches it to weigh contextual cues, not vocabulary.

How do the labeled examples help?

Few-shot examples anchor the model to the kind of irony common in your domain. Reviews use praise contradicted by defects; social media leans on emoji and exaggeration; literature uses mock-authority. Domain-matched examples sharply improve accuracy.

What does the confidence threshold do?

It tells the model not to commit to a sarcastic or ironic label unless its confidence meets your bar. Below it, the text is labeled sincere with a note — useful when false positives are costly.

Does it need an API key?

No. The prompt is built locally in your browser with no network call. You paste your text into the prompt and run it in ChatGPT, Claude, or any model.

Which output format should I choose?

JSON gives a label, confidence, and cue list ideal for pipelines; the explained format adds a one-line rationale for human review; label-only is fastest when you just need the classification.

What is the Socratic Irony Detector Prompt?

Generates a prompt with domain-tuned labeled examples and contextual cue instructions that reliably identify sarcasm and irony even in subtle form, with a configurable confidence threshold and output format. It runs free in your browser on Gera Tools, with nothing uploaded.

Socratic Irony Detector Prompt

Name: Socratic Irony Detector Prompt
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

Socratic irony detector prompt

Sarcasm and irony are where literal sentiment analysis falls apart: the words say one thing and mean the opposite. “Best day ever” after a three-hour queue is mockery, not praise. The Socratic irony detector prompt builds a few-shot prompt that teaches a model to read the contextual cues — contradiction, exaggeration, scare quotes, emoji — that reveal non-literal intent, tuned to the domain you’re working in.

How it works

You pick a domain (social media, product reviews, or literature), which loads labeled examples matched to that style of irony along with an explanation of the cue each one relies on. You set the output format and a confidence threshold below which the model defaults to “sincere” rather than guessing. The tool assembles a prompt listing the contextual cues to weigh — literal-versus-situation contradiction, hyperbole, tonal markers, mock-praise, sentiment-fact mismatch — the few-shot examples, and your threshold and format. It runs locally with no API key; you paste in the text to classify.

Why sarcasm detection is harder than it looks

Standard sentiment classifiers score polarity — positive, negative, neutral — by weighting word frequencies. “Wonderful” scores positive. “Wonderful” written sarcastically after a product catches fire still scores positive, because the model cannot see the fire. Detection requires modeling the relationship between the text and the situation it describes, not just the surface vocabulary.

The three domain options load different cue libraries because the signals differ:

Social media irony often relies on exaggeration and emoji — “Oh great, another delay 🙄” — where the emoji is the irony marker, not the words.
Product reviews use mock-praise contradicted by facts — “Five stars, really loved when the handle snapped off on day two” — where the rating signal and the prose signal point in opposite directions.
Literary and long-form irony often uses elevated register or mock-authority — writing about something trivial in the tone appropriate to an epic — which requires recognising the tonal mismatch.

Worked example

For a product-review classification, the domain examples teach the model patterns like:

“5 stars. I’m so impressed by the charger that heats up hot enough to cook an egg. Highly recommend if you enjoy fire hazards.”

The cue: star rating says positive, but the body describes a dangerous defect in mock-enthusiastic language. The generated prompt instructs the model to recognize the sentiment-fact mismatch and label it as ironic with high confidence, returning in JSON format the label, the cue it triggered on, and the confidence score.

Tips and notes

Match the domain. Review sarcasm and literary irony look different; the wrong example set measurably hurts accuracy on the other domain.
Raise the threshold when false positives cost you. Moderation pipelines often prefer to miss subtle sarcasm than to flag sincere praise as mockery — a high threshold means the “ironic” label only fires on clear cases.
Use JSON output with a cue list. Reading why the model labeled something sarcastic is the fastest way to spot and fix misclassifications when you audit outputs.
Add your own examples. The domain sets are a starting point; appending a few of your hardest real cases as extra few-shot examples before the text to classify consistently lifts accuracy further.