As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language

Problem — This work addresses the gap in understanding how large language models (LLMs) interpret negation within figurative language, a critical aspect of natural language processing that remains underexplored in the literature. The authors highlight that while figurative language and negation are prevalent in communication, existing models struggle with these constructs, particularly when they are combined. This paper is a preprint and has not undergone peer review.

Method — The authors enhance an existing dataset of figurative language by developing a new set of annotations specifically targeting instances of negation. They evaluate a range of LLMs, although specific architectures and model sizes are not disclosed. The evaluation focuses on how different prompt styles affect model performance, suggesting that prompt engineering plays a crucial role in the interpretation of negation. The training compute used for the models is not specified, which limits the reproducibility of the results.

Results — The findings indicate that LLMs exhibit significant variability in performance when interpreting negation in figurative contexts. The authors report that the overall performance is notably influenced by the prompt style, with some prompts yielding better results than others. However, specific performance metrics (e.g., accuracy percentages or F1 scores) against named baselines are not provided, which makes it difficult to quantify the effect sizes or compare against established benchmarks in the field.

Limitations — The authors acknowledge that their study is limited by the reliance on a single dataset, which may not capture the full diversity of figurative language and negation in real-world applications. Additionally, the lack of detailed performance metrics and comparisons to established models limits the ability to assess the significance of their findings. The authors also do not address potential biases in the dataset or the models used, which could affect the generalizability of the results.

Why it matters — Understanding how LLMs interpret negation in figurative language is crucial for improving their applicability in real-world scenarios, such as sentiment analysis, conversational agents, and content generation. The insights from this study could inform future research on enhancing model robustness in handling complex linguistic constructs. This work lays the groundwork for further exploration into prompt engineering and its impact on model performance, as discussed in related literature on LLM interpretability and robustness, as published in arXiv.