How AI Models Interpret Humor, Metaphors, and Analogies

Sarcasm in a support ticket, a dark joke in a chat, or a bold metaphor in ad copy can send LLM figurative language systems into surprising territory. These models sound confident and humanlike, yet they routinely misread non-literal expressions, mixing up what is playful, emotional, or analogical with what is factual or safe to act on.

For teams shipping AI products, that gap between fluent language and shaky figurative understanding is a creative opportunity and a real risk. This article unpacks how large language models interpret humor, metaphors, idioms, and analogies, and then frames those behaviors as a concrete, creative risk analysis, so you can design safer chatbots, summarizers, and content tools without flattening your brand’s voice.

Advance Your Marketing

Inside LLM Figurative Language: Why Non-Literal Meaning Is Hard

Figurative language lets humans compress complex ideas, emotions, and relationships into short, vivid expressions. Metaphors, idioms, analogies, irony, and jokes all ask the listener to move beyond the literal words and infer an intended meaning shaped by shared experience and context.

Large language models, by contrast, are optimized to predict the next token from patterns in massive blocks of text. They learn that certain word sequences tend to follow others and encode rich statistical associations in high-dimensional embeddings, but they are not explicitly designed to track speaker intent, tone, or cultural nuance the way humans do.

How Distributional Learning Collides With Figurative Meaning

Distributional learning treats word meaning as “you are the company you keep,” so models represent a phrase by the contexts in which it appears across the training data. Literal and figurative uses of the same expression often share surface forms but diverge sharply in intent, which can confuse a purely statistical learner.

Take an idiom such as “kick the bucket.” In some sentences, it really does describe a physical action, while in many others, it stands in for death. If the training corpus mixes straightforward explanations, jokes, headlines, and casual conversation, the model must juggle competing patterns that all look plausible when those tokens appear.

Cognitive Theories vs. LLM Mechanics

Cognitive theories of metaphor, such as conceptual metaphor theory, suggest that humans map abstract domains like time, emotion, or obligation onto familiar physical experiences like movement, temperature, or weight. That mapping is grounded in perception and embodiment, not just in text patterns.

Similarly, incongruity-resolution theories of humor frame jokes as a setup that establishes expectations and a punchline that violates them in a way we can reconcile. Navigating that path depends on shared social norms and background knowledge. A language model approximates these processes via text statistics alone, so it can echo the surface form of a metaphor or joke without reliably tracking the underlying conceptual mapping or social calculus.

This structural mismatch between statistical pattern-matching and pragmatic inference is the root of many figurative-language failure modes you see in practice, especially once models are embedded into products that must handle unpredictable, emotionally loaded user input.

How Models Handle Humor, Metaphors, Idioms, and Analogies

To reason about risk, it helps to separate figurative language into broad categories and look at how today’s models typically behave with each. The same underlying architecture will show different strengths and weaknesses depending on whether it is dealing with jokes, metaphors, idioms, or analogies.

Humor and Joke Understanding in LLMs

Modern models can tell jokes, explain jokes, and even generate stand-up-style routines on command, but their sense of humor is brittle. They often misjudge which parts of a story are supposed to be funny versus merely descriptive, and they can easily miss or mishandle sarcasm and dark humor.

Frontier models correctly located the funny segments in stand-up comedy transcripts only about half the time, achieving roughly 50% accuracy on that task. That means the model’s internal “this is the joke” detector fails as often as it succeeds, which is worrying if you rely on it to moderate or transform humorous content.

In a customer support context, that failure looks like misreading “Great job losing my package” as positive sentiment and responding with upbeat congratulations instead of an apology. The underlying issue is that the model sees both praise and complaints around similar words and tends to prioritize the literal polarity of “great” over the pragmatic cue that the situation is negative.

Something similar happens when different cues in the input contradict each other: models tend to “average out” the signals. Work on how LLMs process contradictory FAQs on the same topic shows that they often smooth conflicting instructions into a single, bland answer instead of explicitly flagging inconsistency, which is exactly what you do not want when tone and intent pull in opposite directions.

Metaphors and Idioms: Fluent But Frequently Literal

For common, conventional metaphors and idioms, large language models often look impressive. They can explain phrases like “time is money” or “break the ice” in simple terms, and they usually paraphrase them in contextually appropriate ways.

However, when the task is to generate genuinely figurative language on demand, models frequently revert to safe literal phrasing. 40–60% of outputs intended to be figurative were actually literal, with task accuracy on idiom and metaphor generation benchmarks often below 50%. In other words, the model’s comfort zone is literal exposition, not creative imagery.

Now consider marketing copy built around a metaphor such as “Our platform is your financial co-pilot.” A summarization model might rewrite this as “This software will automatically manage your investments for you,” potentially overstating its capabilities or creating compliance issues. The same systems that interpret brand tone and voice convincingly can still flatten or exaggerate the implications of metaphor-heavy taglines.

Analogies as a Bridge to Deeper Reasoning

Analogies are a structured form of figurative language: instead of a loose image, they establish explicit correspondences between two domains. Many contemporary models perform reasonably well on standardized analogy benchmarks, especially when the analogies follow familiar patterns or appear in templated formats.

In open-ended use, though, analogies remain fragile. If you say “Running a startup is like training for a marathon,” a model can usually elaborate on themes like endurance, pacing, and preparation, but it might miss subtler aspects such as injury risk or the psychology of sticking with a plan when the environment changes. That gap matters when you ask the system to teach, coach, or make recommendations using analogical reasoning as its main tool.

For a product team, the key is to assume that analogies will be unpacked competently when they align with well-traveled text patterns, and much less reliably when they draw on niche domains, subcultures, or novel mappings that push beyond those patterns.

To ground these observations, it helps to see how different figurative categories align with typical model behavior and risk profiles.

Because many models rely heavily on document structure to orient their answers, you can sometimes steer them away from misreading playful copy by separating sections clearly; work on how LLMs use H2s and H3s to generate answers shows that clean headings improve the chances that a model focuses on the right parts of a page when responding.

Creative Risk Analysis: Where Figurative Language Breaks Products

From a product perspective, figurative language is not just a linguistic curiosity; it is a risk vector. Creative phrasing can delight users and differentiate your brand, but it also increases the probability that an AI system will misinterpret intent or emit content that feels off, unsafe, or non-compliant.

Risk Landscape for LLM Figurative Language in Real Products

The risk profile of figurative language depends heavily on context. A playful social chatbot for entertainment can tolerate misread jokes that would be unacceptable in healthcare, finance, or HR. Three levers matter most: the domain you operate in, the vulnerability of your users, and the strictness of the surrounding regulations and expectations.

• Reputational and brand risk: A single ill-judged joke or sarcastic reply can spark screenshots, social backlash, and a perception that your brand is insensitive or unprofessional.
• Safety and psychological risk: Misinterpreting metaphors around self-harm, burnout, or distress can lead to responses that escalate harm or fail to offer needed support.
• Legal and compliance risk: Treating marketing metaphors as literal promises can expose the company to liability under advertising, financial, or medical regulations.
• Factual integrity risk: Analogical or metaphorical reasoning can blur the line between explanation and assertion, eroding the reliability of generated content.
• User experience and trust risk: Tone mismatches, like chirpy replies to angry, sarcastic complaints, make systems feel robotic, unempathetic, and unsafe to rely on.

Imagine a mental health assistant confronted with “I’m drowning at work.” Interpreted figuratively, this might prompt a discussion about boundaries and workload; interpreted literally, it could trigger emergency instructions. The creative expressiveness of the user’s language becomes a hidden branch in your risk tree.

Benchmarks, Red-Teaming, and Cultural Coverage

To manage that risk systematically, you need evidence about how your chosen models behave on figurative tasks, not just anecdotes. Dedicated evaluation work highlights that this behavior can differ sharply from literal-language performance, especially across cultures and low-resource languages.

There’s a 20–40 percentage-point drop in accuracy on figurative-language tasks compared with literal ones, and an additional 15-point degradation in low-resource or cross-cultural settings. Those numbers quantify a gap many practitioners sense intuitively but rarely measure.

In practice, a robust creative risk program layers this kind of off-the-shelf benchmarking with targeted red-teaming tailored to your user population. That means collecting real idioms, jokes, and metaphors from your domain, injecting them into evaluation suites, and tracking how models behave before and after fine-tuning or prompt changes.

In regulated professions such as law, figurative misinterpretation blends into the broader hallucination problem. Guidance on how attorneys can reduce LLM hallucinations about their practice areas is directly relevant here: when a model overreads a metaphorical description of a case, it may fabricate doctrine or precedents to match.

The same pattern appears in security and compliance content. If your documentation mixes claims like “SOC 2 certified” with softer phrases such as “military-grade security,” a model may blur those lines. Insights into how LLMs interpret security certifications and compliance claims can help you separate metaphorical flourishes from verifiable statements that models should treat literally.

Detection Pipelines and Guardrails That Actually Reduce Risk

One of the most effective mitigation patterns is to explicitly detect figurative language and route it through specialized handling, rather than relying on a single, general-purpose prompt to do the right thing. Recent research introduces scenario-anchored topological scoring as a real-time detector for non-literal utterances.

Figurative-language detection inserted at the front of a pipeline reduced figurative-interpretation error rates by 28% and unsafe humor interpretations by 35% compared with a baseline GPT-4-style prompting setup. That kind of improvement is hard to squeeze out of prompt tweaks alone.

• Classify intent early: Flag user inputs and intermediate generations that look figurative, ambiguous, or humor-laden before you generate final replies or actions.
• Route conditionally: Send figurative utterances through specialized prompts that ask for clarification, paraphrase the input literally, or constrain response style.
• Filter systematically: Apply stricter safety and content filters to humor and sarcasm, especially in domains where offense or harm carry high cost.
• Log edge cases: Capture borderline figurative-language interactions for human review, and recycle them into fine-tuning or evaluation sets.

Agencies that specialize in AI-era search and content strategy, such as Single Grain, increasingly weave these figurative-language detectors and guardrails into broader SEVO, AEO, and CRO programs so that generative experiences stay on-brand, safe, and compliant without becoming dull.

If you are planning to launch or scale an AI assistant and want expert help designing figurative-language-aware prompts, guardrails, and evaluation, you can get a FREE consultation to assess your current risk posture and roadmap.

Advance Your Marketing

Strategic Takeaways on LLM Figurative Language for Leaders

From an executive or product-lead vantage point, figurative language is where the gap between “sounds human” and “reliably understands humans” is most visible. Large models can handle many straightforward metaphors and analogies, but show sharp performance cliffs when culture, emotion, or high stakes enter the conversation.

Practical Tests for LLM Figurative Language Performance

Before you trust a model with real users, run targeted tests that treat figurative behavior as a first-class requirement rather than an afterthought. A lightweight evaluation suite goes a long way toward exposing brittle spots.

1. Collect real metaphors, idioms, jokes, and analogies from your domain: support tickets, sales calls, community posts, or training transcripts.
2. Design prompts that mirror realistic flows: user input with figurative language, system reply, and any follow-up clarifications you expect.
3. Rate outputs along several axes: did the model interpret intent correctly, avoid offensive or dismissive tone, and keep factual claims grounded?
4. Test across user segments and languages where applicable, watching for drops in quality on region-specific idioms or humor.
5. Feed your findings into a prioritized backlog of prompt changes, guardrails, and documentation edits that reduce reliance on fragile interpretations.

Analogies deserve special attention in this process. When you use them to teach or explain, ensure that the model’s elaborations do not oversimplify to the point of misinformation or inadvertently stretch the analogy beyond its useful range.

Lightweight Design Patterns to Reduce Figurative Misfires

Not every team can build custom detectors or retrain models, but you can still reduce figurative-language risk through careful interaction design and prompt engineering. Small structural choices in how you ask the model to behave often pay large dividends.

• Ask for literal paraphrases: Before answering, have the model restate user input in plain, literal language, then base its reasoning on that paraphrase.
• Separate “fun” from “facts”: In UX, clearly distinguish playful, metaphor-heavy sections from factual or instructional areas, and instruct the model to treat them differently.
• Constrain humor by default: Unless your product is explicitly comedic, discourage spontaneous jokes or sarcasm; let users opt into humor rather than forcing it.
• Require explanations of non-literal reads: When the model detects an idiom or metaphor, have it briefly explain its interpretation before taking actions that depend on it.
• Document domain-specific idioms: Provide style guides or glossaries of key metaphors and analogies in your prompt context so the model learns how your organization uses them.

Ultimately, LLM-generated figurative language is both an asset and a liability. It enables richer, more human-feeling interactions, but it also exposes blind spots that can hurt users and brands if left unmanaged. Treating figurative understanding as a design dimension, alongside latency, accuracy, and cost, helps you make deliberate trade-offs instead of reacting to incidents.

If you want generative experiences that are witty without being reckless, expressive without being misleading, and optimized for SEVO and AEO as well as user safety, Single Grain can help you build them. Get a FREE consultation to evaluate your current AI stack, uncover figurative-language risks, and design a roadmap that turns LLM figurative language strengths into a competitive advantage instead of a hidden liability.

Advance Your Marketing