Can one model handle all types of figurative language?
Does treating metaphor, idioms, and irony as a single pragmatic reasoning task—rather than separate classification problems—offer a more unified and effective approach to figurative language understanding in LLMs?
The standard NLP approach to figurative language treats each device as a separate problem: metaphor detection, idiom classification, sarcasm identification, irony recognition. Each gets its own dataset, its own benchmark, its own model architecture. But this fragmentation misses a structural unity: all figurative language involves the same underlying operation — recovering literal meaning from non-literal expression.
The Diplomat dataset formalizes this reframing. Across 4,177 dialogues with 6,494 human-annotated answers, it treats metaphors, idioms, and puns as instances of a single pragmatic reasoning task: given a figurative expression in a situated context, what is the speaker actually communicating? (Diplomat: A Dialogue Dataset for Situated PragMATic Reasoning)
This matters because it changes what we think LLMs need to do. If figurative language is a collection of separate classification problems, then the solution is more training data per category. If figurative language is a unified pragmatic reasoning task, then the solution is better pragmatic inference — the ability to reason about what a speaker intends versus what they literally say.
Since Do large language models reason symbolically or semantically?, metaphor is a particularly interesting test case. Metaphor is decoupled semantics: using one domain's vocabulary (vehicles, journeys, combat) to illuminate another (relationships, careers, arguments). The decoupling that causes LLM reasoning collapse is the defining feature of metaphorical language. If the unified pragmatic framing holds, then improving LLM performance on figurative language requires improving their ability to handle semantic decoupling — which is precisely the dimension where current architectures struggle most.
The practical implication for literary analysis: rather than building separate tools for metaphor extraction, irony detection, and idiom interpretation, a unified pragmatic reasoner could approach the full range of figurative devices in literary texts through a single inference mechanism. Whether current LLM architectures can support this remains an open question — but the Diplomat dataset provides the evaluation framework.
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- Does AI struggle with poetry for the same reason it misses jokes?
- Can language models adapt irony detection to specific communicative contexts?
- What happens when LLMs analyze literary irony that relies on understatement?
- How do politeness strategies depend on semantic ambiguity between literal and intended meaning?
- Can LLMs improve at metaphor if they handle decoupled semantics better?
- How does implicit meaning processing limit LLM pragmatic reasoning?
- Why do language models overestimate irony likelihood in emoji use?
- Do metaphors work by decoupling meaning from linguistic associations?
- Can LLMs identify implicit metaphoric mappings that require pragmatic inference?
- How does the inability to manage ambiguity undermine literary analysis tasks?
- How much semantic meaning survives when LLMs paraphrase poetry and literary text?
- Why do cognitive metaphors change based on available technology?
Related concepts in this collection 4
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Do large language models reason symbolically or semantically?
Can LLMs follow explicit logical rules when those rules contradict their training knowledge? Testing whether reasoning operates independently of semantic associations reveals what computational mechanisms actually drive LLM multi-step inference.
metaphor as the paradigmatic case of decoupled semantics
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Can language models adapt implicature to conversational context?
Do large language models flexibly modulate scalar implicatures based on information structure, face-threatening situations, and explicit instructions—as humans do? This tests whether pragmatic computation is truly context-sensitive or merely literal.
scalar implicature as another pragmatic reasoning task in the unified frame
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Why do speakers deliberately use ambiguous language?
Explores whether ambiguity is a linguistic defect or a strategic tool speakers use for efficiency, politeness, and deniability. Matters because it challenges how we train language systems.
figurative language leverages productive ambiguity
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Why does ChatGPT fail at implicit discourse relations?
ChatGPT excels when discourse connectives are present but drops to 24% accuracy without them. What does this gap reveal about how LLMs actually process meaning and logical relationships?
implicit meaning processing is the bottleneck for pragmatic reasoning
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Diplomat: A Dialogue Dataset for Situated PragMATic Reasoning
- Meanings are like Onions: a Layered Approach to Metaphor Processing
- Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom
- Semantic Change Characterization with LLMs using Rhetorics
- The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
- Potemkin Understanding in Large Language Models
- Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
- Computational structuralism: Toward a formal theory of meaning in the age of digital intelligence
Original note title
figurative language can be treated as a unified pragmatic reasoning task rather than separate classification problems for metaphor idiom and irony