Unpacking Metaphors: AI Analysis in Literature

This study aims to pull out Metaphors and Analogies from literary texts, which is no easy task. These elements need advanced reasoning skills, such as abstraction and language grasping. We created a unique Dataset with the help of experts to extract metaphoric analogies. Our work compares how well large language models (LLMs) can identify these analogies from text fragments that contain proportional analogies. We also check if these models can fill in missing parts of the analogy that readers can guess but are not stated directly in the text.

#The Importance of Analogies

Analogies are crucial for thinking because they help humans create Concepts. Extracting them from free text is tricky since they can suggest hidden concepts and connect very different ideas. For instance, saying "My head is an apple without a core" maps “head” to “apple” and suggests “brains” as an implicit term. Recent advancements in LLMs offer a way to better handle these metaphoric elements in Natural Language Processing.

#Why Study Metaphors and Analogies?

While humans can learn from a limited number of examples, LLMs struggle with this kind of reasoning. Analogical thinking helps to generalize and abstract concepts, which is essential for understanding literature. Some models show promise in this area, but we need to see if they can manage complex metaphoric analogies in texts.

#Dataset Construction

We built a dataset with 204 examples of sourced metaphors. Each instance includes the pairs of concepts forming an analogy. We manually selected short texts that show proportional analogies, ensuring they fit into our framework. Each example includes concepts that either appear explicitly in the text or that need to be inferred.

#Task Definition

Our main tasks are to extract the explicit elements of an analogy and identify both the source and target domains. We also aim to generate concepts that fill in any missing elements. Our focus is on short, well-known literary texts. We expect to pull out pairs of expressions that show the relationship defined as “T1 is to T2 what S1 is to S2.”

#Related Work

Research about metaphors spans many fields. While some focus on daily language, our interest is in literary metaphors. We believe that these expressions carry more complex meaning and provide a rich ground for analysis. Previous studies have explored how to find and map these metaphors, but we aim to evaluate the ability of LLMs to handle this in a structured way.

#Experiment Setup

For our experiments, we ran tests with various LLMs, including models like GPT-3.5 and GPT-4. The task involved prompting the models with specific texts and asking them to extract the four concepts that form the analogy. We looked at how well these models could generate relevant terms when certain items in the analogy were not clearly stated.

#Evaluation Methods

We evaluated how well the models extracted the terms and generated implicit concepts. Accuracy was measured in several ways, including exact matches and overlap. We also included human judgment to assess the generated terms, giving scores based on how well the terms made sense.

#Results and Discussion

Our results show that models like GPT-4 performed well in extracting explicit terms from texts. However, the generation of implicit terms revealed a larger gap. While many terms were relevant, there's room for improvement. The models fared better with some frames over others, and fewer nouns in the text often led to better performance.

#Limitations

One major limitation of our study is the small size of the dataset. Furthermore, while we used various metrics for assessment, a manual evaluation of the extracted terms could provide an additional layer of understanding.

#Conclusion

In summarizing our findings, we see that LLMs can be effective tools in converting unstructured metaphors into organized analogical mappings. Despite some challenges, the impressive results suggest many future paths for research in this area. We hope to build on this work, looking to incorporate the relationships between concepts and testing the models on more complex texts.

#Future Work

Future studies could look to enhance performance by expanding the dataset and refining how we prompt the models. The long-term goal is to improve the extraction of analogies and metaphors, making these tools even more useful for understanding literature and other texts.

#Ethical Considerations

Throughout our study, we did not find significant ethical concerns. However, as with all AI tools, the potential for misleading or incorrect outputs remains. Caution is advised when interpreting results or deploying these tools in real-world settings.

#Acknowledgments

We express gratitude to those who contributed to this project, including annotators and reviewers. Their feedback was invaluable in shaping our research.