Venue: NAACL 2024

# A Theory Guided Scaffolding Instruction Framework

for LLM-Enabled Metaphor Reasoning

 Yuan Tian\({}^{1,2}\), Nan Xu\({}^{1,3}\), Wenji Mao\({}^{1,2}\)

\({}^{1}\)State Key Laboratory of Multimodal Artificial Intelligence Systems,

Institute of Automation, Chinese Academy of Sciences

\({}^{2}\)School of Artificial Intelligence, University of Chinese Academy of Sciences

\({}^{3}\)Beijing Wenge Technology Co., Ltd

{tianyuan2021,xunan2015,wenji.mao}@ia.ac.cn

Corresponding author

###### Abstract

Metaphor detection is a challenging task in figurative language processing, which aims to distinguish between metaphorical and literal expressions in text. Existing methods tackle metaphor detection via training or fine-tuning discriminative models on labeled data. However, these approaches struggle to explain the underlying reasoning process behind the metaphorical/literat judgment. Recently, large language models (LLMs) have shown promise in language reasoning tasks. Although promising, LLM-based methods for metaphor detection and reasoning are still faced with the challenging issue of bringing the explainable concepts for metaphor reasoning and their linguistic manifestation. To fill this gap, we propose a novel **T**heory guided **S**caffolding Instruction (TSI) framework that instructs an LLM to infer the underlying reasoning process of metaphor detection guided by _metaphor theories_ for the first time. Our work is inspired by a pedagogical strategy called _scaffolding instruction_, which encourages educators to provide questioning and support as scaffolding so as to assist learners in constructing the understanding of pedagogical goals step by step. We first construct a metaphor knowledge graph grounded in metaphor theory, which serves as the instructional structure to obtain a series of scaffolding questions, directing the LLM to incrementally generate the reasoning process for metaphor understanding through dialogue interactions. During this theory guided instruction process, we explore the LLM's mastery boundary and provide the relevant knowledge as scaffolding support when the question is beyond the LLM's capability. Experimental results verify that our method significantly outperforms both the LLM-based reasoning methods and the SOTA methods in metaphor detection, indicating the facilitation of metaphor and instruction theories in guiding LLM-enabled reasoning process.

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Footnote †: leftmargin=*] (a) Standard Zero-Shot

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Footnote †: leftmargin=*] (b) Zero-Shot Chain-of-Thought

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Footnote †: leftmargin=*] (c) Prompt with Key Ideas from Conceptual Metaphor Theory

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Footnote †: leftmargin=*] (d) Prompt with Key Ideas from Conceptual Metaphor Theory

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Footnote †: leftmargin=*] (e) Prompt with Key Ideas from Conceptual Metaphor Theory

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Footnote †: leftmargin=*] (f) Prompt with Key Ideas from Conceptual Metaphor Theory

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Footnote †: leftmargin=*] (g) Prompt with Key Ideas from Conceptual Metaphor Theory

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Footnote †: leftmargin=*] (h) Zero-Shot Chain-of-Thought

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Footnote †: leftmargin=*] (i) Prompt with Key Ideas from Conceptual Metaphor Theory

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Footnote †: leftmargin=*] (h) Zero-Shot Chain-of-Thought


chine translation Mao et al. (2018), sentiment and emotion analysis Mao and Li (2021), and conversational dialogue Sun et al. (2023).

Existing studies on metaphor detection establish supervised learning methods based on labeled data Rohanian et al. (2020); Le et al. (2020); Mao and Li (2021). Recently, some studies employ metaphor theories to help design models for metaphor detection, resulting in significant performance gains Zhang and Liu (2022); Ge et al. (2022); Tian et al. (2023). Although these methods are inspired by different metaphor theories, they essentially train or fine-tune discriminative models to learn a decision boundary between metaphorical and literal samples, lacking the ability to explain the underlying reasoning process of metaphorical/literal judgment.

Recently, large language models (LLMs) have shown promise in generating reasoning processes with natural language across various tasks Kojima et al. (2022); Wang et al. (2023); Zhang et al. (2023), which demonstrates their potential to provide the underlying reasoning process for metaphor understanding. Although promising, LLM-based methods for metaphor detection and reasoning are still faced with the challenging issue of bringing the explainable concepts for metaphor reasoning and their linguistic manifestation. Figure 1 gives examples of LLM-based metaphor detection methods. Figure 1 (a) and (b) show that LLMs fail to detect the metaphor; even when employing zero-shot chain-of-thought method Kojima et al. (2022). This limitation may be attributed to the fact that metaphors are not merely linguistic phenomena but associated with human thought and reasoning process Lakoff and Johnson (1980). Thus metaphors inherently convey intricate and implicit meanings that require a deeper level of understanding, making it more difficult for LLMs to comprehend. To better understand and detect metaphors, metaphor theories that provide well-established frameworks can serve as shortcuts to help effective metaphor reasoning and understanding. Figure 1 (c) provides an LLM with the prompt incorporated with the key ideas from conceptual metaphor theory CMT Lakoff and Johnson (1980), the key metaphor theory in cognitive linguistics. The result shows that, with the help of CMT theory, the LLM can provide a correct answer with an explanation of corresponding reasoning process.

In this paper, we tackle the challenging issue of LLM-based metaphor reasoning. To better release the capability of LLMs for metaphor reasoning and understanding, we make the first attempt to incorporate well-founded metaphor theories in such process. Further, to bridge the abstract conceptual representations of metaphor theories and the detailed LLM reasoning process, we draw inspiration from a pedagogical strategy called scaffolding instruction Bruner (1974); Vygotsky (1978), which emphasizes that an educator continually explores a learner's mastery boundary through dialogues and designs questions and support as scaffolding, to assist learners in constructing the understanding of pedagogical goals step by step. To achieve this, we propose a novel Theory guided Scaffolding Instruction framework TSI for LLM-enabled metaphor reasoning. Specifically, we rely on three representative metaphor theories, selection preference violation SPV Wilks (1975, 1978), metaphor identification procedure MIPP Pragglejaz Group (2007), and conceptual metaphor theory CMT Lakoff and Johnson (1980). In our computational construct, we first formalize a metaphor theory by converting it into a metaphor knowledge graph KG, which functions as the instructional structure to construct scaffolding questions that help LLM's metaphor reasoning process. During dialogue interactions, our framework automatically detects the mastery boundary of LLM and provides relevant knowledge as scaffolding support when the question is beyond the LLM's capability.

Our main contributions are as follows:

* To release the metaphor reasoning capability of LLMs, we make the first attempt to propose a theory guided framework TSI and develop the conceptual representations by converting metaphor theories into knowledge graphs for scaffolding instruction.
* The scaffolding instruction process in TSI constructs prompts for scaffolding questions based on metaphor KGs to facilitate the step-by-step reasoning, and provides scaffolding support by dynamically assessing the LLM's mastery boundary.
* Experimental results on two datasets verify that our method achieves significant performance gains compared to LLM-based methods in complex reasoning and the SOTA methods in metaphor detection 

Related Work

Metaphor TheoriesAs a fundamental and profound figurative phenomenon in linguistics and cognition, metaphor has been theorized across various disciplines in linguistics, philosophy and psychology (Plato, 1901; Kittay, 1990; Aristotle, 1995). However, not all of these theories are appropriate for computational formalization. Among them, three theories are favorable in existing computational research on metaphor, including selectional preference violation (SPV) (Wilks, 1975), metaphor identification procedure (MIP) (Pragglejaz Group, 2007), and conceptual metaphor theory (CMT) (Lakoff and Johnson, 1980).

The SPV theory suggests that the disparity between the _context_ of a word within a sentence and its frequently used contexts is an indicator of this word's metaphorical usage. The MIP theory aims to standardize the metaphor annotation process, emphasizing that a metaphor is identified if the contextual meaning of the word differs from its basic _meaning_. Unlike SPV and MIP, which utilize indicative clues based on linguistic features of contexts or word meanings to identify metaphors, CMT goes beyond linguistic analysis and proposes a cognitive basis for metaphor understanding, suggesting that metaphor facilitates a mapping of attributes from _source domain_ to _target domain_ in human cognition. In this paper, we focus on releasing the metaphor reasoning capability of LLMs with the guidance of these three metaphor theories.

Computational Work on Metaphor without TheoriesComputational studies on metaphor focus on three main tasks, including metaphor detection, metaphor interpretation, and metaphor generation. Most of the work, especially the early work, established methods for computational metaphor processing without consideration of metaphor theories.

Metaphor detection, which attracts more research attention compared to the other two tasks, aims to identify an expression in text as metaphorical or literal. Early researchers utilize machine learning based methods to detect metaphors (Tsvetkov et al., 2014; Shutova et al., 2016; Bulat et al., 2017). Given that understanding metaphors requires knowledge beyond the context, some studies employ external knowledge resources for this task (Rohanian et al., 2020; Wan et al., 2021; Li et al., 2023). Benefiting from multitask learning, other studies learn shared information between metaphor detection and related tasks (Le et al., 2020; Mao and Li, 2021; Zhang and Liu, 2023; Badathala et al., 2023). Moreover, some studies (Lin et al., 2021; Feng and Ma, 2022) explore data augmentation methods to alleviate the problem of insufficient data for this task.

Metaphor interpretation involves explaining the implicit meaning conveyed by a metaphorical expression, which is a more challenging task than metaphor detection. Existing studies without using metaphor theories often formulate this task as a paraphrasing task to generate literal substitute paraphrase for a metaphorical word/phrase (Shutova, 2010; Shutova et al., 2012; Zayed et al., 2020) or identify the literal interpretation for a metaphorical expression in a candidate sentence set (Bizzoni and Lappin, 2018; Chakrabarty et al., 2022).

Metaphor generation focuses on generating metaphorical expressions from literal ones. Some studies generate a metaphorical word to replace the literal one using the end-to-end generation framework (Yu and Wan, 2019; Chakrabarty et al., 2021). Other studies generate the metaphorical sentence based on syntactic patterns (Brooks and Youssef, 2020) or a given Chinese noun (Li et al., 2022).

Computational Work on Metaphor with TheoriesUnlike the above research only focusing on computational perspectives for metaphor processing, recently, some researchers (Stowe et al., 2021b; Ge et al., 2022) have incorporated well-established metaphor theories into computational work on metaphor and achieved promising results.

Some studies (Su et al., 2021; Song et al., 2021; Choi et al., 2021; Zhang and Liu, 2022; Wang et al., 2023b) adopt SPV and MIP in network design for metaphor detection. Inspired by CMT, Ge et al. (2022) propose a model that generates plausible source and target concepts to help metaphor detection. Tian et al. (2023) further propose an attribute Siamese network to learn similar attributes between the source and target domains for this task.

In metaphor interpretation, some research considers the source domain implied by the metaphor in CMT as the interpretation and develops various methods to identify these domains, such as the unsupervised method (Shutova et al., 2017), deep learning based method (Rosen, 2018) and LLM-based method (Wachowiak and Grommann, 2023). Other research extracts attributes that link source and target domains as interpretation of metaphors (Su et al., 2017; Rai et al., 2019; Su et al., 2020).

In metaphor generation, Stowe et al. (2021b) 

make the first attempt to generate metaphors with conceptual mappings grounded in CMT and develop the lexical model and seq-to-seq model for metaphor generation. In addition, Stowe et al. (2021) focus on comparing free and controlled metaphor generation based on CMT.

Although previous methods achieve promising results, they only apply metaphor theories for network design or model the elements in these theories, lacking the ability to explicitly reflect the inherent reasoning processes grounded in theories. Recently, large language models (LLMs) have shown potential for reasoning and can generate reasoning processes in natural language, while how to develop the LLM's capability on metaphor reasoning remains unexplored. Thus, we focus on releasing LLM's ability on metaphor reasoning and understanding with the guidance of metaphor theories.

Complex Reasoning with LLMsIn recent years, LLMs have shown multiple emergent abilities Wei et al. (2022), leading the shift of paradigm in natural language processing from fine-tuning to in-context learning. However, LLMs still exhibit limitations when they tackle complex reasoning tasks. To mitigate this gap, Wei et al. (2022) propose chain-of-thought (CoT) prompting with task-specific exemplars of reasoning processes. After that, some studies design automatic CoT prompting methods encouraging LLMs to generate reasoning processes in a zero-shot manner Kojima et al. (2022); Wang et al. (2023); Zhang et al. (2023). Other studies explore enhancement methods to improve the reasoning ability of current CoT methods, such as refinement Madaan et al. (2023), question decomposition Zhou et al. (2023), voting Wang et al. (2023), ranking Khalifa et al. (2023) and using external knowledge Zhao et al. (2023). Cognitive linguists argue that metaphors are not just linguistic expressions but fundamental to human thought and cognition Lakoff and Johnson (1980), thus understanding metaphors requires complex reasoning processes in the human brain. Although the above research has explored many complex reasoning tasks, LLM-enabled metaphor reasoning is still a challenging task. Therefore, in this paper, we make the first attempt to design methods to unleash the potential of LLM on metaphor reasoning.

## 3 Problem Definition

Formally, \({\cal D}_{te}\)=\(\{(s_{k},w_{k},l_{k})\}^{N_{te}}_{k=1}\) is the test dataset with \(N_{te}\) instances, where \(s_{k}\) is a sentence, \(w_{k}\) is a word within \(s_{k}\), and \(l_{k}\) is the label (metaphorical or literal) for \(w_{k}\). Our goal of metaphor detection with LLM is to predict the label of the word within a sentence in \({\cal D}_{te}\) without any training data.

## 4 Method

We propose a novel **T**heory guided **S**caffolding **I**nstruction framework (TSI) for LLM-enabled metaphor reasoning. Under the guidance of a metaphor theory, our framework instructs LLM to explicitly give a reasoning process with questioning and support as scaffolding to determine whether the word within a sentence is metaphorical or literal. Figure 2 shows the overall architecture of our framework, which contains four primary components: (1) _Metaphor Knowledge Graph Construction_, which represents a simplified metaphor theory and computational aspects in a graphical format; (2) _Scaffolding Question Construction_, which constructs a sequence of scaffolding questions for an LLM with metaphor knowledge graph as the instructional reference; (3) _Scaffolding Support based on Mastery Level of LLM_, which automatically detects the current capability of LLM (i.e. mastery level) and provides knowledge as support if necessary; and (4) _Classification_, which categories the word within the sentence as metaphorical or literal, based on the comparison between the structure of knowledge graph constructed from LLM's reasoning processes and the structure of metaphor knowledge graph.

### Metaphor Knowledge Graph Construction

To facilitate theory guidance and computational construction for LLM-enabled metaphor reasoning, we manually simplify the metaphor theory and construct a metaphor knowledge graph (KG), which covers the essential information in both the theory and computational aspects. On the theory side, metaphor KG represents the fundamental concepts and the relation between concepts identified in the metaphor theory. On the computational side, metaphor KG also represents the core linguistic expressions of the word, sentence, and POS information as well as their connections. Specifically, the metaphor KG is formulated as \({\cal G}=\{{\cal E},{\cal R},{\cal F}\}\), where \({\cal E}\), \({\cal R}\), and \({\cal F}\) represent sets of entities, relations, and facts, respectively. There are two groups of entities in our metaphor KG. One group consists of all the entities of concepts \(\{e_{k}^{c}\}^{N_{c}}_{k=1}\) mentioned 

in metaphor theory or used for computational usage, and the other group consists of all the entities of attributes \(\{e_{k}^{a}\}_{k=1}^{N_{a}}\) associated with the concepts in metaphor KG. The relation could be a directed relation \(r^{d}\) or an undirected relation \(r^{u}\) between entities. The \(i\)-th fact \(f_{i}\in\mathcal{F}\) is denoted as a triple \((e^{h},r,e^{t})\), where \(e^{h}\) and \(e^{t}\) denote head and tail entities, respectively. We apply our framework to three metaphor theories widely used in computational metaphor processing, including selectional preference violation (SPV) Wilks (1975, 1978), metaphor identification procedure (MIP) Pragglejaz Group (2007), and conceptual metaphor theory (CMT) Lakoff and Johnson (1980). The details of metaphor KGs constructed based on the simplification of SPV, MIP and CMT in our methods are shown in Appendix B.

### Scaffolding Question Construction

Questioning is a commonly used approach in the pedagogical strategy scaffolding instruction Zheng et al. (2019); Laili and Siswono (2020), which helps ascertain the learner's current level of understanding, stimulate critical thinking and encourage deeper understanding. Inspired by this, taking a sentence and a word within the sentence as initial information, we construct a sequence of questions as scaffolding with the metaphor KG as the reference guiding LLM to obtain metaphor reasoning processes. Each scaffolding question induces a reasoning process based on a fact in the metaphor KG. We have two types of reasoning processes, which are as follows:

* _Forward Reasoning_: In a fact (e.g. \(fact_{1}\) and \(fact_{2}\) in Figure 2), given an instantiated head entity of concept (along with its associated attribute entities, if applicable) and a direct relation in the metaphor KG, the LLM needs to determine the unknown instantiated tail entity;
* _Relation Reasoning_: In a fact (e.g. \(fact_{3}\) in Figure 2), given two instantiated entities of concepts (along with their associated attribute entities, if applicable) in the metaphor KG, the LLM needs to infer whether the undirected relation between these two entities exists.

For the forward reasoning, we use a Wh-question to construct the scaffolding question. For the relation reasoning, we use the template "Decide whether ..." to construct the scaffolding question. We construct a sequence of scaffolding question prompts \(\mathcal{P}=[p_{1},p_{2},\ldots,p_{K}]\) according to the sequence of facts \(\mathcal{F}=[f_{1},f_{2},\ldots,f_{K}]\) within the metaphor KG. Details of these scaffolding question prompts for LLMs based on CMT, MIP and SPV, respectively, are illustrated in Appendix B.

Figure 2: Overall architecture of our proposed framework TSI guided by conceptual metaphor theory for LLM-enabled metaphor reasoning. _[answer i]_ denotes the input slot of the final answer for the scaffolding question \(p_{i}\).

 



### Scaffolding Support based on Mastery Level of LLM

Scaffolding instruction also emphasizes the importance of providing support to learners when they engage in tasks beyond their _masterry levels_, which means what a learner can do independently Wood et al. (1976); Vygotsky (1978); Harris and Pressley (1991). Inspired by this, we devise an approach to automatically detect the mastery level of LLM and provide knowledge as scaffolding support if the question is beyond the current capability of LLM. Figure 2 shows the overall architecture of this approach, which is a pipeline framework, including a mastery level verifier, a metaphor knowledge scaffolding module, and a filtering module. Appendix B gives the pseudocode of scaffolding support based on mastery level of LLM.

Mastery Level VerifierWhen humans are confused or lack confidence in answering a question, they tend to provide inconsistent responses when they are asked the same question multiple times Schaeffer and Presser (2003), as they may simply guess. Inspired by this observation, we propose the mastery level verifier by examining the consistency of the answers that the LLM provides to estimate the LLM's masterly level. For the \(i\)-th fact, given the scaffolding question prompt \(p_{i}\), the set of inputs \(\mathcal{X}_{i}=\{x_{(i,1)},x_{(i,2)},\ldots,x_{(i,N_{i})}\}\) for this question, and an LLM denoted as \(\mathcal{M}\), our framework generates the answer \(a_{i}\) formulated as

\[a_{i}=\mathcal{M}(p_{i}\|\mathcal{X}_{i}), \tag{1}\]

where \(N_{i}\) is the number of inputs, and we perform Eq. (1) for \(N_{o}\) times with temperature \(t_{p}\), yielding a set of answers \(\mathcal{A}_{i}=\{a_{(i,1)},a_{(i,2)},\ldots,a_{(i,N_{o})}\}\). \(r_{c}\in(0.5,1]\) is the consistency ratio and \(n_{min}=\lfloor N_{o}\times r_{c}+0.5\rfloor\) is the minimum number of consistent answers, where \(\lfloor.\rfloor\) denotes the floor function. The specific designs of our verifier for different reasoning processes are as follows:

* _Forward Reasoning:_ We use T5 Raffel et al. (2020) to calculate a semantic textual similarity score between every answer pair in \(\mathcal{A}_{i}\), and obtain the similarity matrix \(\mathbf{S}^{N_{o}\times N_{o}}\). If there exists a submatrix \(\mathbf{B}=\mathbf{S}[j_{1},j_{2},\dots,j_{n};j_{1},j_{2},\dots,j_{n}]\) and \(s\geq s_{min}\) for \(\forall s\in\mathbf{B}\), where \(\{j_{1},j_{2},\dots,j_{n}\}\) represents the indices of the \(n\) selected answers, \(n\geq n_{min}\), and \(s_{min}\) is the semantic similarity threshold, we obtain the set of consistent answers \(\mathcal{A}_{i}^{c}=\{a_{(i,j_{1})},a_{(i,j_{2})},\ldots,a_{(i,j_{n})}\}\).
* _Relation Reasoning:_ We first project the answers into "Yes", "No", or "Uncertain" with rules. If there exists a subset of answers \(\mathcal{A}_{i}^{c}=\{a_{(i,j_{1})},a_{(i,j_{2})},\ldots,a_{(i,j_{n})}\}\) and all elements in \(\mathcal{A}_{i}^{c}\) can be projected into the same answer ("Yes" or "No"), where \(\{j_{1},j_{2},\dots,j_{n}\}\) represents the indices of the \(n\) selected answers, and \(n\geq n_{min}\), we regard \(\mathcal{A}_{i}^{c}\) as the set of consistent answers.

If there exist consistent answers \(\mathcal{A}_{i}^{c}\), we regard the question falls within the mastery level of LLM; otherwise, the question is beyond the LLM's ability.

Metaphor Knowledge ScafffoldingIf the scaffolding question \(p_{i}\) surpasses the mastery level of the LLM, this module provides a scaffolding knowledge prompt \(b_{i}\) from the metaphor knowledge base (KB) to help the LLM answer this question. The detail of metaphor KB is shown in Appendix B. Given the scaffolding question prompt \(p_{i}\), inputs \(\mathcal{X}_{i}=\{x_{(i,1)},x_{(i,2)},\ldots,x_{(i,N_{i})}\}\) for this question, scaffolding knowledge prompt \(b_{i}\), and the LLM \(\mathcal{M}\), the metaphor knowledge scaffolding module generates the answer \(a_{i}^{s}\), which is formulated as

\[a_{i}^{s}=\mathcal{M}(p_{i}\|\mathcal{X}_{i}\|b_{i}). \tag{2}\]

We perform Eq. (2) for \(N_{k}\) times with temperature \(t_{p}\) resulting in a set of outputs \(\mathcal{A}_{i}^{s}=\{a_{(i,1)}^{s},a_{(i,2)}^{s},\ldots,a_{(i,N_{k})}^{s}\}\).

FilteringWe randomly select one answer from the remaining answers of \(\mathcal{A}_{i}^{s}\) after denoising or \(\mathcal{A}_{i}^{c}\) as the final answer \(a_{i}^{o}\).

### Classification

Based on all the final reasoning answers of the LLM, we can construct an instantiated knowledge graph \(\hat{\mathcal{G}}\) for the original input sentence and a word within the sentence. If the structure of \(\hat{\mathcal{G}}\) is identical to that of \(\mathcal{G}\), we label this instance as metaphorical; otherwise, we label it as literal.

## 5 Experiments

### Datasets

We conducted experiments on two publicly available metaphor datasets: (1) **MOH-X** Mohammad et al. (2016) comprises 647 sentences, where only a single verb is annotated as metaphorical or literal in each sentence; (2) **TroFi** Birke and Sarkar (2006) is another metaphor detection dataset, collected from 1987-1989 Wall Street Journal Corpus

We sampled 300 balanced instances from MOH-X and 300 balanced instances from TroFi for testing. Table 1 shows the statistics of datasets.

### Baseline Methods

We use representative methods for LLM-based complex reasoning as baselines, including the standard zero-shot method, automatic chain-of-thought methods, and chain-of-thought enhancement methods, which are as follows: (1) **Standard zero-shot**[25] prompts the LLM to provide answers directly in a zero-shot manner; (2) **Zero-shot CoT**[46] concatenates the question prompt with a simple trigger sentence (i.e. "_Let's think step by step_") encouraging LLM to generate the reasoning process in a zero-shot manner; (3) **Plan-and-solve**[78] designs a prompt aimed at guiding LLM to solve the problem by breaking the task into smaller subtasks, and then implementing the devised plan; (4) **Self-refine**[9] improves initial outputs from LLM through iterative feedback and refinement; (5) **Self-consistency**[78] selects the most consistent answer through majority voting among sampled reasoning chains; (6) **Least-to-most**[13] initially decomposes the question into sub-questions in a top-down manner and then addresses each sub-question.

We also use the SOTA methods for metaphor detection as strong baselines, which are as follows: (1) **MelBERT**[15] leverages contextualized information inspired by MIP and SPV for metaphor detection; (2) **HisNet**[16] which incorporates MIP and SPV into their linguistics enhanced network; (3) **AdMul**[16] is the SOTA method for metaphor detection, which employs a multi-task learning framework to transfer knowledge from basic sense discrimination to metaphor detection via adversarial training. We use these methods trained on a large metaphor dataset VUA All [14] to perform zero-shot transfer on our datasets.

### Implementation Details

We employ the accuracy and macro-average F1 for evaluation and report the mean and standard deviation of 3 runs in our experiments. The OpenAI GPT-3.5 (gpt-3.5-turbo-0613)1 serves as the LLM in our experiments. The details of rules to filter answers in our method are shown in Appendix B. The prompt design for the baselines and other implementation details are shown in Appendix C.2

Footnote 1: [https://platform.openai.com/docs/models/gpt-3-5](https://platform.openai.com/docs/models/gpt-3-5)

Footnote 2: Our codes are available at [https://github.com/TIAN-viola/TSI](https://github.com/TIAN-viola/TSI).

### Main Results

Comparison with BaselinesTable 2 shows the comparative results between our methods and baselines. Our methods guided by MIP and CMT achieve significant improvements over all the representative LLM-based methods in complex reasoning, which verifies the effectiveness of our scaffolding instruction framework for LLM-enabled metaphor reasoning. Least-to-most, tackling problems through sub-questions, outperforms other baselines on MOH-X. Zero-shot CoT, encouraging LLM to think step by step, achieves better results than other baselines on TroFi. Both of these methods guide LLM to tackle problems step by step, indicating the necessity of deep and sequential thinking in metaphor reasoning.

Compared with SOTA methods in metaphor detection, our method guided by CMT outperforms them across all datasets, and our method guided by MIP also achieves better performances on TroFi. These results further verify the effectiveness of our method. Although MelBERT and MisNet design their networks under the guidance of metaphor theories and fine-tune pre-trained models for metaphor detection, they rely on capturing surface-level clues in these theories and fail to capture the underlying metaphor reasoning process reflected in these theories. This limitation results in our method guided by MIP and CMT achieving significant improvements over MelBERT and MisNet.

Comparison between Metaphor TheoriesThe experimental results in Table 2 indicate that our method guided by SPV performs worse compared to our methods guided by MIP and CMT. One possible reason is that as some metaphorical usages become prevalent over time, SPV, relying on data fre

\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\#Instance** & **\%Met.** & **Avg. L** & **\#Samp** \\ \hline MOH-X & 647 & 48.7 & 8.0 & 300 \\ TroFi & 3737 & 43.5 & 28.3 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of datasets. **#Instance** represents the number of all the instances. **%Met.** represents the percentage of metaphorical instances. **Avg. L** denotes the average length of instances. **#Samp** denotes the number of randomly sampled instances utilized for evaluation.

 

quency rather than semantic essence, tend to lead our method to wrong results on such metaphorical usages. In addition, our method using MIP performs better than our SPV guided method. This improvement seems to benefit from the fact that understanding contextual meaning is a strength of LLM, which is an important step in our method guided by MIP. In contrast to SPV and MIP, which analyze metaphor at the linguistic level, CMT mainly leverages source and target domains to explain implicit comparisons in metaphors reflected in human cognition, which seems to contribute to the superior performances of our method guided by CMT across all datasets. These results also indicate the potential of the comprehensive perspectives in metaphor theories, especially CMT, for enhancing computational work on metaphor.

### Ablation Study

We conduct the ablation study to evaluate the impact of components in our methods, using the following variants: (a) **w/o Metaphor Knowledge Scaffolding** removes the _metaphor knowledge scaffolding_ module in our method from our methods; (b) **w/o Mastery Level Verifier** ablates _masteryer level verifier_ and its subsequent _metaphor knowledge scaffolding_ module; (c) **w/o Metaphor Knowledge Graph** removes the metaphor KG and presents LLM with a single question prompt. This prompt comprises a description of key ideas in the metaphor theory and a direct query about whether the word in the sentence is metaphorical, templates of which are shown in Appendix C.

Table 3 shows the results of our ablation study.

\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**MOH-X**} & \multicolumn{2}{c}{**TroFi**} \\ \cline{2-5}
**Method** & F1 & Acc. & F1 & Acc. \\ \hline \multicolumn{5}{|l|}{_Methods in LLM-based complex reasoning_} \\ Standard zero-shot Wei et al. [46] & \(66.43\pm 1.45\) & \(69.11\pm 1.29\) & \(58.58\pm 1.01\) & \(61.22\pm 0.95\) \\ Zero-shot CoI Kojima et al. [7] & \(70.97\pm 0.37\) & \(71.22\pm 0.32\) & \(64.18\pm 1.07\) & \(64.78\pm 0.95\) \\ Plan-and-Solve Wang et al. [10] & \(54.27\pm 2.73\) & \(57.89\pm 2.01\) & \(58.13\pm 2.29\) & \(58.33\pm 2.23\) \\ Self-refine Madaan et al. [10] & \(64.06\pm 0.37\) & \(67.67\pm 0.27\) & \(57.27\pm 0.22\) & \(60.89\pm 0.16\) \\ Self-consistency Wang et al. [10] & \(70.65\pm 1.58\) & \(72.22\pm 1.50\) & \(60.30\pm 0.76\) & \(61.56\pm 0.68\) \\ Least-to-most Zhou et al. [10] & \(74.43\pm 1.27\) & \(75.00\pm 1.25\) & \(63.88\pm 3.16\) & \(64.11\pm 3.03\) \\ \hline \multicolumn{5}{|l|}{_Methods in metaphor detection (zero-shot transfer)_} \\ MelBERT Choi et al. [7] & \(77.88\pm 0.83\) & \(77.89\pm 0.83\) & \(62.36\pm 1.51\) & \(62.89\pm 1.29\) \\ MisNet Zhang and Liu [11] & \(77.08\pm 1.12\) & \(77.11\pm 1.13\) & \(62.01\pm 0.64\) & \(62.67\pm 0.54\) \\ AdvMul Zhang and Liu [10] & \(79.74\pm 0.44\) & \(79.89\pm 0.42\) & \(60.54\pm 1.43\) & \(62.67\pm 0.98\) \\ \hline \multicolumn{5}{|l|}{_Our methods guided by different metaphor theories_} \\ TSI (SPV) & \(74.22\pm 1.95\) & \(74.33\pm 1.96\) & \(51.93\pm 1.60\) & \(56.22\pm 1.03\) \\ TSI (MIP) & \(79.39\pm 0.35\) & \(79.44\pm 0.31\) & \(65.60\pm 1.08\) & \(65.89\pm 1.03\) \\ TSI (CMT) & \(\mathbf{82.59\pm 2.22}\) & \(\mathbf{82.93\pm 1.94}\) & \(\mathbf{66.07\pm 1.11}\) & \(\mathbf{66.89\pm 1.13}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between our methods and baselines. The best results are highlighted in bold font. The best results for baselines in LLM-based complex reasoning and those in metaphor detection are underlined

\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{2}{c}{**MOH-X**} & \multicolumn{2}{c}{**TroFl**} \\ \cline{2-4}
**Variant** & F1 & Acc. & F1 & Acc. \\ \hline TSI (SPV) & \(\mathbf{74.22\pm 1.95}\) & \(\mathbf{74.33\pm 1.96}\) & \(\mathbf{51.93\pm 1.60}\) & \(\mathbf{56.22\pm 1.03}\) \\ w/o Metaphor Knowledge Scaffolding & \(73.08\pm 1.30\) & \(73.22\pm 1.34\) & \(50.94\pm 1.54\) & \(55.56\pm 1.26\) \\ w/o Masters Jevel Verifier & \(72.30\pm 1.58\) & \(72.44\pm 1.59\) & \(50.13\pm 1.17\) & \(55.11\pm 0.96\) \\ w/o Metaphor Knowledge Graph & \(69.11\pm 0.25\) & \(69.44\pm 0.31\) & \(51.78\pm 1.00\) & \(53.33\pm 0.72\) \\ \hline TSI (MIP) & \(\mathbf{79.39\pm 0.35}\) & \(\mathbf{79.44\pm 0.31}\) & \(\mathbf{65.60\pm 1.08}\) & \(\mathbf{65.89\pm 1.03}\) \\ w/o Metaphor Knowledge Scaffolding & \(70.36\pm 1.30\) & \(71.33\pm 1.19\) & \(65.40\pm 0.91\) & \(65.78\pm 0.63\) \\ w/o Masters Jevel Verifier & \(69.86\pm 0.72\) & \(71.00\pm 0.82\) & \(65.16\pm 0.40\) & \(65.22\pm 0.42\) \\ w/o Metaphor Knowledge Graph & \(69.11\pm 0.25\) & \(69.44\pm 0.31\) & \(58.69\pm 0.87\) & \(58.78\pm 0.83\) \\ \hline \hline TSI (CMT) & \(\mathbf{82.59\pm 2.22}\) & \(\mathbf{82.93\pm 1.94}\) & \(\mathbf{66.07\pm 1.11}\) & \(\mathbf{66.89\pm 1.13}\) \\ w/o Metaphor Knowledge Scaffolding & \(79.11\pm 0.69\) & \(79.11\pm 0.68\) & \(64.28\pm 0.15\) & \(65.33\pm 0.27\) \\ w/o Masters Jevel Verifier & \(79.07\pm 0.53\) & \(79.11\pm 0.57\) & \(63.43\pm 1.05\) & \(64.67\pm 0.98\) \\ w/o Metaphor Knowledge Graph & \(75.55\pm 1.77\) & \(76.11\pm 1.73\) & \(62.50\pm 2.32\) & \(62.63\pm 2.10\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of ablation study.

 

Removing the metaphor knowledge scaffolding module reduces the performance, thus verifying the effectiveness of the knowledge base we provide in our method. Furthermore, we remove the mastery level verifier from our methods, resulting in significant performance declines across all the datasets and theories. These variants demonstrate the effectiveness of the approach to provide scaffolding support based on detecting the mastery level of LLM in our method. In addition, to evaluate the effectiveness of the metaphor KG representation, we directly ablate it and present LLM with the theory description and a query to ask whether the word in the sentence is metaphorical. This variant uses the in-context learning ability of LLM to learn metaphor theory, which leads to sharp drops in performance across all the datasets, indicating that LLM can learn a metaphor theory better through the representation of metaphor knowledge graphs rather than theory descriptions.

### Hyper-parameter Analysis

Number of AnswersTo explore the impact of the number of answers generated by LLM (denoted as \(N_{o}\)) in our methods, we experiment on varying \(N_{o}\) from 1 to 11. The experimental results in Figure 3 show that increasing \(N_{o}\) initially enhances the performance, followed by a plateau or marginal decline of the performance.

Consistency RatioTo analyze the impact of the consistency ratio \(r_{c}\) in our mastery level verifier, we conduct experiments varying \(r_{c}\) from 0.6 to 1. The experimental results in Figure 4 indicate that a high consistency ratio could bring performance drops for the reason that an appropriate ratio allows LLM to make a slight number of incorrect answers.

Semantic Similarity ThresholdTo evaluate the influence of semantic similarity threshold \(s_{min}\) in our mastery level verifier, we conduct experiments on varying the threshold from 1.5 to 5.0, where a higher score indicates greater semantic similarity. From the experimental results in Figure 5, we can see that our method guided by CMT achieves the optimal performance at lower thresholds, while our method guided by MIP excels with larger thresholds. This phenomenon might be attributed to the variance in the quality of knowledge presented in the metaphor knowledge Scaffolding module. The knowledge derived from MIP seems to be more comprehensible for LLM in comparison to the knowledge derived from CMT.

## 6 Conclusion

In this paper, we propose a theory guided scaffolding instruction framework for LLM-enabled metaphor reasoning. Inspired by a pedagogical strategy _scaffolding instruction_, our framework instructs an LLM to infer the metaphor reasoning process using questioning and support as scaffolding guided by a metaphor theory. Experimental results show that our framework outperforms the previous LLM-based complex reasoning methods and the SOTA methods for metaphor detection, verifying the effectiveness of our proposed framework and indicating the facilitation of metaphor and instruction theories in LLM-enabled reasoning.

Figure 4: Results of our methods with different consistency ratios on MOH-X and TroFi.

Figure 5: Results of our methods with different semantic similarity thresholds on MOH-X and TroFi.

Figure 3: Results of our methods with different numbers of answers generated by LLM on MOH-X and TroFi.

 



### Limitations

Our work has some limitations. Firstly, we are unable to evaluate our framework on large-scale test sets due to the costs of experimentation with LLMs, thus we obtain balanced randomly sampled test datasets for the evaluation of this work. In addition, we simplify metaphor theories by focusing on fundamental concepts, their relations and corresponding knowledge in our method. However, the metaphor theories that we use, especially CMT, contain rich contexts, which are worth further exploration in LLM-enabled metaphor reasoning. Compared to the current pretrained model based work on metaphor detection, our work can be viewed as opening a new path to the theory-directed, instruction-oriented LLM-based metaphor understanding and reasoning. To this end, we hope our work can promote future research on further enriching knowledge representation based on the comprehensive perspectives in metaphor theories, forming a more in-depth understanding of metaphors and incorporating it into the LLM-enabled reasoning process through novel methodologies, by peer researchers and ourselves.

## Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grants #72293575 and #62206287. We thank the anonymous reviewers for the valuable comments.

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## Appendix A Licenses of Scientific Artifacts

The OpenAI API for gpt-3.5-turbo-0613 is available at [https://openai.com/api/](https://openai.com/api/). The license of T5 (Raffel et al., 2020) is Apache-2.0 and the license of MOH-X (Mohammad et al., 2016) is available at [https://saifmohammad.com/WebPages/SentimentEmotionLabeledData.html](https://saifmohammad.com/WebPages/SentimentEmotionLabeledData.html). The license of TroFi (Birke and Sarkar, 2006) is available at [https://www2.cs.sfu.ca/~anoop/students/jbirke/LICENSE.html](https://www2.cs.sfu.ca/~anoop/students/jbirke/LICENSE.html).

## Appendix B Method Details

Metaphor Knowledge Graph ConstructionTo facilitate theory guidance and computational construct for LLM-based metaphor reasoning, we make the compromise and simplify the metaphor theory, retaining the fundamental concepts and their relations in our metaphor knowledge graph representations. Specifically, Figure 6 illustrates the details of metaphor knowledge graphs constructed based on the computational elements and relations, as well as the simplified theories, including CMT, MIP, and SPV in our methods.

Prompt Design for Scaffolding QuestionsTable 4 summarizes a list of template prompts for scaffolding questions \({\cal P}\) in our methods using different metaphor theories.

Scaffolding Support based on Mastery Level of LLMTable 5 shows the rules in our methods to project the answers of LLM for the relation reasoning process into "Yes", "No", or "Uncertain" in scaffolding support based on the mastery level module. If there are any candidate phrases in Table 5 that appear in the answer after converting all the uppercase characters in the answer string into lowercase characters, we project this answer into the corresponding category. If none of these candidate phrases appear in the answer, we check the answer manually. Table 6 shows details about the template prompts of the metaphor knowledge base in the metaphor knowledge scaffolding module in our methods using different metaphor theories. Algorithm 1 is the pseudocode of scaffolding support based on mastery level of LLM.

FilteringWe first convert all the uppercase characters in the answer string into lowercase characters and then filter out the uncertain answers generated from the metaphor knowledge scaffolding module that contains any of the following phrases: "it is not possible to determine", "cannot be determined", "is uncertain" and "difficult to provide a definitive answer".

## Appendix C Implementation Details

Table 7 shows the hyperparameter values of our methods in our experiments on different datasets. We use T5-3B1 to calculate the semantic textual similarity score between the answer pair. The number of sampled reasoning paths for the baseline Self-consistency is 40. Table 8 summarizes a list of prompts for baselines in our experiments. Table 9 illustrates the template prompts for the variant of _w/o Metaphor Knowledge Graph_ in the ablation study. If there is no knowledge prompt for a question, we just use the majority voter to replace scaffolding support based on mastery level of LLM module and get the final answer for this question. The system prompt that we use for GPT-3.5 in the experiments of our methods and baselines is as follows.

 

You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.

Knowledge cutoff: 2021-09

Current date: 2023-10-15

\begin{table}
\begin{tabular}{|p{48.4pt} p{367.9pt}|} \hline \hline
**Theory** & **Question** \\ \hline \multirow{7}{*}{CMT} & **Question**\(p_{1}\) & The source domain is a conceptual domain containing concepts that are typically concrete, tangible, and familiar to us. What is the source domain implied by the [POS] ”word”? \\ \cline{2-3}  & **Question**\(p_{2}\) & The target domain is a conceptual domain that contains concepts that are typically vague and abstract. In the sentence “sentence”, what is the target domain that the word ”word” tries to describe? \\ \cline{2-3}  & **Question**\(p_{3}\) & Here it’s a sentence ”sentence”. Now we know the source domain implied by the [POS] ”word” and the target domain that the word ”word” tries to describe in this sentence. \\ \cline{2-3}  & **Question**\(p_{3}\) & [answer 1] \\ \cline{2-3}  & **Question**\(p_{1}\) & [answer 2] \\ \cline{2-3}  & **Question**\(p_{2}\) & Based on the above information, decide whether the source domain is different from the target domain? \\ \hline \multirow{7}{*}{MIP} & **Question**\(p_{1}\) & What is the basic meaning of the [POS] ”word”? \\ \cline{2-3}  & **Question**\(p_{2}\) & In the sentence “sentence”, what is the contextual meaning of the word ”word”? \\ \cline{2-3}  & **Question**\(p_{3}\) & Here is a sentence ”sentence”. Now we know the basic meaning of the [POS] ”word” and the contextual meaning of the word ”word”. \\ \cline{2-3}  & **Question**\(p_{3}\) & [answer 1] \\ \cline{2-3}  & **Question**\(p_{3}\) & Based on above information, decide whether the contextual meaning of the word ”word” is different from its basic meaning. \\ \cline{2-3}  & **Question**\(p_{1}\) & Please provide examples of the frequent usage of the [POS] ”word”. \\ \cline{2-3}  & **Question**\(p_{2}\) & Now we know examples of the frequent usage of the [POS] ”word”. \\ \cline{2-3}  & **Question**\(p_{2}\) & [answer 1] \\ \cline{2-3}  & **Question**\(p_{1}\) & In the sentence “sentence”, decide whether the context of the word ”word” is different from its contexts in the above sentences. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Template prompts for scaffolding questions \(\mathcal{P}\). _[sentence]_ and _[word]_ denote the input slots for the sentence in an instance and the target word within this sentence, respectively. _[POS]_ denotes the input slot of the part of speech of the target word. _[answer i]_ denotes the input slot for the LLM’s final answer to question \(p_{i}\).

Figure 6: Metaphor knowledge graphs guided by CMT, MIP and SPV.

 

```
0: (1) \({\cal M}\): an LLM; (2) \(\operatorname{Combs}({\cal L},k)\): obtains combinations of all elements within a set \({\cal L}\) taken \(k\) elements at a time.
0: (1) \({\cal A}_{i}\): set of \(N_{o}\) answers of \({\cal M}\) for the \(i\)-th scaffolding question prompt \(p_{i}\); (2) \(r_{c}\): consistency ratio; (3) \(s_{min}\): semantic similarity threshold; (4) \(b_{i}\): the scaffolding knowledge prompt for \(p_{i}\); (5) \({\cal X}_{i}\): inputs for \(p_{i}\).
0:\(a^{o}_{i}\): final answer for the \(i\)-th scaffolding question \(p_{i}\).
1: Compute \(n_{min}=\lfloor N_{o}\times r_{c}+0.5\rfloor\)\(\triangleright\)**Mastery Level Verifier**
2:if\(p_{i}\) is for forward reasoning then
3: Compute \({\bf S}^{N_{o}\times N_{o}}\) with semantic textual similarity scores between every pair of answers in \({\cal A}_{i}\)
4:for all\(j\gets N_{o}\)to\(n_{min}\)do
5:for all\(I_{j}\in Combs([1,\ldots,N_{o}],j)\)do
6:if\(\forall s\in{\bf S}[I_{j},I_{j}],s\geq s_{min}\)then
7:\({\cal A}^{c}_{i}={\cal A}_{i}[I_{j}]\), break
8:if\({\cal A}^{c}_{i}\) exists then break
9:else
10: Obtain \({\cal A}^{p}_{i}\) by project answers in \({\cal A}_{i}\) to "Yes", "No" or "Uncertain" with rules
11:if number of "Yes" in \({\cal A}^{p}_{i}\geq n_{min}\)then
12: Retrieve the index list \(I\) of element "Yes" within \({\cal A}^{p}_{i}\)
13:\({\cal A}^{c}_{i}={\cal A}_{i}[I]\)
14:else
15:if number of "No" in \({\cal A}^{p}_{i}\geq n_{min}\)then
16: Retrieve the index list \(I\) of element "No" within \({\cal A}^{p}_{i}\)
17:\({\cal A}^{c}_{i}={\cal A}_{i}[I]\)
18:if\({\cal A}^{c}_{i}\) does not exists then\(\triangleright\)Metaphor Knowledge Scaffolding
19: Compute \(a^{s}_{i}={\cal M}(p_{i}\|{\cal X}_{i}\|b_{i})\) for \(N_{k}\) times \(\triangleright\) Eq. (2)
20: Obtain \({\cal A}^{s}_{i}\) by denoising answers in \(\{a^{s}_{(i,1)},a^{s}_{(i,2)},\dots,a^{s}_{(i,N_{k})}\}\)
21: Sample an answer \(a^{o}_{i}\) from \({\cal A}^{s}_{i}\)\(\triangleright\)Filtering
22:else
23: Sample an answer \(a^{o}_{i}\) from \({\cal A}^{c}_{i}\)\(\triangleright\)Filtering 



\begin{table}
\begin{tabular}{c c p{34.1pt} p{286.7pt}} \hline \hline
**Answer** & **Category** & **Candidate Phrase** \\ \hline  & & Yes,; Yes.; is/are (indeed/likely/most likely/conceptually/generally/inherently/potentially/ fundamentally) different; can/might/seems to/appears to/may/could/would (indeed) be (considered) different/distinct; may/appears to/might/can/could/does/would differ; differs from; do not align; are/is not (inherently/conceptually/generally/potentially) the same; the source (domain) and (the) target domains/domain differ \\ \cline{2-5} Answer for question \(q_{3}\) (CMT) & No:, No.; is/are (likely/conceptually/generally/inherently/potentially) the same; can/might/seems to/may/could/would (indeed) be (considered) the same; is/are (likely/conceptually/generally/inherently/potentially/closely) similar/aligned/related; is/are not indeed/likely/most (likely/conceptually/generally/inherently/potentially/fundamentally) different/disinct \\ \cline{2-5}  & Uncertain & it’s difficult to provide a definitive answer; it is not possible to determine; without further context/information/clarification; need further clarification/context \\ \hline  & & Yes,; Yes.; is/are (indeed/likely/most likely/conceptually/generally/inherently/potentially/ fundamentally) different; can/might/seems to/apears to/may/could/would (indeed) be (considered) different/distinct; may/appears to/might/can/could/does/would differ; differs from; do not align; are/is not (inherently/conceptually/generally/potentially) the same; goes beyond the/its basic meaning; diverges from; can/might/seems to/appears to/may/could/would (indeed) deviate \\ \cline{2-5} Answer for question \(q_{3}\) (MIP) & No & No:, No.; is/are (likely/conceptually/generally/inherently/potentially) the same/equivalent; can/might/seems to/appears to/may/could/would (indeed) be (considered) the same; is/are (likely/conceptually/generally/inherently/potentially/closely) similar/aligned/related; is/are not indeed/likely/most (likely/conceptually/generally/inherently/potentially) different/distinct; corresponds with its/the basic meaning; is used in its/the basic meaning \\ \cline{2-5}  & Uncertain & it’s difficult to provide a definitive answer; it is not possible to determine; without further context/information/clarification; need further clarification/context \\ \hline  & & Yes,; Yes.; is/are (indeed/likely/most likely/conceptually/generally/inherently/potentially/ fundamentally) different; can/might/seems to/may/could/would (indeed) be (considered) different/distinct; may/appears to/mind/can/could/does/would differ; differs from; do not align; are/is not (inherently/conceptually/generally/potentially) the same; does suggest a different usage; implies a different context; is used differently in this context \\ \cline{2-5} Answer for question \(q_{2}\) (SPV) & No & No:, No.; is/are (likely/conceptually/generally/inherently/potentially) the same; the same; is/are (likely/conceptually/generally/inherently/potentially/closely) similar/aligned/related; is/are not indeed/likely/most (likely/conceptually/generally/inherently/potentially/fundamentally) different/distinct; would represent/be a similar usage; can/might/seems to/appears to/may/could/would (indeed) be consistent with its frequent usage; is used in the same context; in a context similar to its frequent usage; falls under the first/second/third example; is related to its usage in the first/second/third example \\ \cline{2-5} Uncertain & It’s difficult to provide a definitive answer; it is not possible to determine; without further context/information/clarification; need further clarification/context \\ \hline \hline \end{tabular}
\end{table}
Table 5: Rules to project the answers of LLM in the relation reasoning process into “Yes”, “No” or “Uncertain".





\begin{table}
\begin{tabular}{p{14.2pt} p{370.6pt} p{59.8pt} p{59.8pt} p{59.8pt} p{59.8pt} p{59.8pt}} \hline
* More concrete; what they evoke is easier to imagine, see, hear, feel, smell, and taste. &
- Related to bodily action. & \\  & **Knowledge**\(b_{1}\) & & More precise (as opposed to vague) &
- Historically older. &
- Historically older. &
- Taking the knowledge provided above into account, please answer the following question: \\ \cline{2-7}  & **Knowledge**\(b_{3}\) & & The contextual meaning means the meaning of the lexical unit in context, that is, how the lexical unit applies to an entity, relation, or attribute in the situation evoked by the text. Take into account what comes before and after the lexical unit. The contextual meaning may be conventionalized and will thus be found in a general users’ dictionary. It may also be novel or specialized and will thus not be found in a general users’ dictionary. &
- This knowledge provided above into account, please answer the following question: \\ \cline{2-7}  & **Knowledge**\(b_{1}\) & & If the basic meaning of a word contrasts with its contextual meaning, there is a difference as well as comparison between the contextual and a more basic meaning. &
- As well as comparing between the knowledge provided above into account, please answer the following question: \\ \hline SPV & **Knowledge**\(b_{2}\) & & The phrase “frequent usage of a word” refers to the regular and common application of a word. &
- The phrase “frequent usage of a word” refers to the regular and common application of a word.




\begin{table}
\begin{tabular}{c l} \hline \hline
**Method** & **Template Prompt** \\ \hline Standard zero-shot & Decide whether the word “[word]” in the sentence ”[sentence]" is used metaphorically. \\ \hline Zero-shot CoT & Decide whether the word “[word]” in the sentence ”[sentence]” is used metaphorically. Let’s think step by step. \\ \hline Plan-and-Solve & Decide whether the word “[word]” in the sentence ”[sentence]” is used metaphorically. Let’s first understand the problem and devise a plan to solve the problem. Then, let’s carry out the plan and solve the problem step by step. \\ \hline Self-refine & _Prompt for Freedback:_ Can you give suggestions to improve the above answer? If the answer is perfect, just return “NONE”. \\  & _Prompt for Refine:_ Okay, let’s use this feedback to improve the above answer. \\ \hline Self-consistency & Decide whether the word “[word]” in the sentence ”[sentence]” is used metaphorically. \\ \hline Least-to-most & _Prompt for Stage 1: Decompose Question into subquestions_ Q: Is the word “[word]” in the sentence ”[sentence]” used metaphorically? \\  & A: Let’s break down this problem into subquestions: \\ \cline{2-3}  & _Prompt for Stage 2: Sequentially Solve Subquestions_ Q: Is the word “[word]” in the sentence ”[sentence]” used metaphorically? \\  & A: Let’s break down this problem into subquestions: \\  & [answer] \\  & Q: Please answer the above subquestions and give the final answer for the initial question ”Is the word ‘[word]’ in the sentence ’[sentence]’ used metaphorically?” \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prompt design for LLM-based baselines. _[sentence]_ denotes the input slot for the sentence in an instance from the test dataset and _[word]_ denotes the input slot for the target word within this sentence. _[answer]_ denotes the input slot for the answer of LLM to the previous question.

\begin{table}
\begin{tabular}{l p{456.9pt}} \hline \hline
**Theory** & **Template Prompt** \\ \hline CMT & According to conceptual metaphor theory, metaphor facilitates a mapping of attributes or characteristics from the source domain to the target domain. The source domain is a conceptual domain containing concepts that are typically concrete, tangible, and familiar to us. The target domain is a conceptual domain containing concepts that are typically vague and abstract. Based on the above information, answer the following question: In the sentence, ”[sentence]”, decide whether the word ”[word]” is used metaphorically. \\ \hline MIP & According to metaphor identification procedure, a metaphor is identified if the contextual meaning of the target word is different from its more basic meaning. Based on the above information, answer the following question: In the sentence, ”[sentence]”, decide whether the word ”[word]” is used metaphorically. \\ \hline SPV & According to selectional preference violation, a metaphor is identified by noticing the difference between the context of a target word and its frequently used contexts. Based on the above information, answer the following question: In the sentence, ”[sentence]”, decide whether the word ”[word]” is used metaphorically. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prompt design for the variant of _w/o Metaphor Knowledge Graph_. _[sentence]_ and _[word]_ denote the input slots for the sentence in an instance and the target word within this sentence, respectively.