Large Language Models Show Potential in Detecting Sarcasm


New York University researcher, Juliann Zhou, conducted a study to evaluate the effectiveness of Large Language Models (LLMs) in detecting sarcasm. Sarcasm refers to the use of ironic statements to convey ideas opposite to what is actually intended. Properly identifying sarcasm is crucial for sentiment analysis in Natural Language Processing, as it provides insights into people’s true opinions.

Previous research has used language representation models like Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) to identify sarcasm based on contextual information. However, advancements in NLP and the development of LLMs offer new possibilities for sarcasm detection.

In recent years, there have been efforts to develop models specifically designed to detect sarcasm in written texts. Two notable models, CASCADE and RCNN-RoBERTa, were introduced by different research groups in 2018.

CASCADE, proposed by Hazarika et al, is a context-driven model that has shown promising results in sarcasm detection. Additionally, the BERT model introduced by Devlin et al in 2018 offers higher precision in interpreting contextualized language. Zhou’s study aimed to compare the performance of CASCADE and RCNN-RoBERTa models in detecting sarcasm.

To evaluate the models, Zhou conducted tests using comments posted on Reddit, a popular online platform for discussions and content rating. The performance of CASCADE and RCNN-RoBERTa was compared to the average human performance on the same task and to baseline models for text analysis.

The study found that incorporating contextual information, such as user personality embeddings, significantly improved the models’ performance. Additionally, using a transformer RoBERTa showed better results compared to a more traditional CNN approach. The study suggests that augmenting transformers with additional contextual information features could be a promising avenue for future experiments.

The findings from Zhou’s study hold potential for further advancements in detecting sarcasm and irony in human language using LLMs. Improved sarcasm detection models can be valuable tools for sentiment analysis, allowing for more accurate assessments of online reviews, posts, and other user-generated content.

As LLMs become increasingly widespread, understanding their capabilities and limitations is crucial. Evaluating the effectiveness of these models in specific tasks, like sarcasm detection, can aid in identifying areas for improvement and optimizing their use in various applications.

1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it