Investigating the Impact of Conceptual Metaphors on LLM -based NLI through Shapley Interactions (2025)

Abstract: Metaphorical language is prevalent in everyday communication, often used unconsciously, as in “rising crime.” While LLMs excel at identifying metaphors in text, they struggle with downstream tasks that implicitly require correct metaphor interpretation, such as natural language inference (NLI). This work explores how LLMs perform on NLI with metaphorical input. Particularly, we investigate whether incorporating conceptual metaphors (source and target domains) enhances performance in zero-shot and few-shot settings. Our contributions are two-fold: (1) we extend metaphorical texts in an existing NLI dataset by source and target domains, and (2) we conduct an ablation study using Shapley values and interactions to assess the extent to which LLMs interpret metaphorical language correctly in NLI. Our results indicate that incorporating conceptual metaphors often improves task performance.

## introduction metaphorical language is pervasive in the flow of everyday life conversations. while metaphors can be observed in explicit cases such as "she was the light of my life", the meaning manifestation of conventionalized metaphors such as "tax the rich" is more fundamentally grounded in language (kövecses, 2010; @xcite . according to @xcite , metaphorical meaning construction is the result of a conceptual metaphor mapping that connects one domain to another: a concept is taken from a source domain to explain a target domain. for example, in "gun addicts increasingly realize that society is rejecting them", the source domain is addiction and the target domain guns. despite progress in computational metaphor interpretation @xcite , large language models (llms) underperform on downstream tasks that require correct interpretation premise the news just got you all hyped. ## related work existing nlp methods dealing with metaphors interpret them by cognitively decoding conceptual metaphors. @xcite applied hierarchical clustering and conceptual mappings, while @xcite paraphrased metaphors using framenet-based source and target domains. @xcite leveraged these domains for textual entailment. the actual inference of conceptual metaphors was first studied by @xcite who used contrastive learning to infer source domains from metaphorical texts. @xcite extended this by multitask learning to jointly predict source domains and the aspects highlighted by metaphors. unlike prior work, we focus on interpreting implicit metaphors, beyond their explicit detection. we use nli, a core nlp reasoning task @xcite , where metaphorical meaning is embedded naturally, as in everyday language. this enables a more realistic test of llms' metaphor understanding, viewed here as synthesizing cognitively grounded facts @xcite . we thus analyze llms' metaphor interpretation via nli. we build on the flute dataset @xcite , where many nli hypotheses are metaphorical. prior work benchmarked transformers on flute, revealing the difficulty of implicit figurative tasks. we emphasize the importance of context and conceptual metaphor interactions, addressing this by analyzing llm performance using shapley-based methods @xcite . ## data in this section, we present our extension, flute.st, of the flute dataset by source and target domains. ## results and analysis local explanations figure 3 shows local sv and si explanations of two example instances correctly predicted as entailment and contradiction by few-shot mistral. from the svs, we observe mixed but high contributions of the source domain, target domain, and explanation. in both cases, the explanation has a high influence toward the predicted class. the si explanations suggest that higherorder effects greatly influence the predictions. individually, the components point toward the wrong class. however, the plot reveals a high shapley value (sv) for the metaphor "a war" and its source domain conflict/violence in the entailment case, and for "killing the past" and its source domain death/rebirth in the contradiction case. from the perspective of conceptual metaphor theory (cmt), this indicates that the model leverages the systematic mappings between source and target domains to anchor its reasoning. rather than treating these metaphors as isolated lexical items, the model appears to recognize their entailments within the broader conceptual structures they evoke-for example, understanding war in terms of struggle or opposition, and death/rebirth in terms of negation and renewal. the elevated svs highlight that these mappings are not peripheral but central to the model's decision-making process, shaping the direction of inference. this suggests that the model is engaging in a form of metaphorical reasoning that parallels human interpretive strategies, constructing meaning by projecting structure from concrete domains onto abstract targets. consequently, the results provide evidence that the model's predictions in figurative nli may arise from mechanisms resembling conceptual integration and reasoning, rather than simple pattern imitation. ## conclusion this paper studied the role of conceptual metaphors (source and target domains) in llm performance on natural language inference (nli) in case of texts with implicit metaphorical language. to this end, we extended the flute dataset with source and target domain annotations in our new dataset flute.st. based on the dataset, we ran zero-shot and few-shot ablations across five llms. our results suggest that explanations of nli combined with conceptual metaphors yield the best performance, supported by a shapley-based analysis. future work should explore advanced techniques leveraging metaphorical knowledge to improve llms' understanding of implicit metaphors.