Can large language models detect sarcasm?

Large language models (LLMs) are advanced deep learning algorithms that can analyze prompts in various human languages, then generating realistic and comprehensive responses. This promising class of natural language processing (NLP) models has become increasingly popular after the release of Open AI’s ChatGPT platform, which can quickly answer a wide range of user queries and generate compelling written texts for different uses.

As these models become more prevalent, it is of utmost importance to evaluate their capabilities and limitations. These assessments can ultimately help understand the situations in which LLMs are most or least useful, while also identifying ways in which they could be improved.

Juliann Zhou, a researcher at New York University, recently conducted a study to evaluate the performance of two LLMs trained to detect human sarcasm, which involves conveying ideas by ironically stating the exact opposite of what they mean. ‘we’re trying to say. His findings, published on the preprint server arXivhelped her delineate features and algorithmic components that could improve the sarcasm detection capabilities of AI agents and robots.

“In the field of sentiment analysis of natural language processing, the ability to correctly identify sarcasm is necessary to understand people’s true opinions,” Zhou wrote in his paper. “As the use of sarcasm is often context-based, previous research has used language representation models, such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), to identify sarcasm with contextual information. Recent innovations in NLP have provided more possibilities for detecting sarcasm.”

Sentiment analysis is a field of research that involves analyzing text typically posted on social media platforms or other websites to better understand what people think about a particular topic or product. Today, many companies are investing in this area because it can help them understand how to improve their services and meet the needs of their customers.

There are now several NLP models that can process texts and predict their underlying emotional tone, that is, whether they express positive, negative or neutral emotions. Many reviews and comments posted online, however, contain irony and sarcasm, which could cause models to classify them as “positive” when they are actually expressing negative emotion, or vice versa.

Some computer scientists have thus attempted to develop models capable of detecting sarcasm in written texts. Two of the most promising models, called CASCADE and RCNN-RoBERTa, were presented in 2018 by separate research groups.

“In BERT: Pre-training Deep Bidirectional Transformers for Language Understanding, Jacob Devlin et al (2018) introduced a new language representation model and demonstrated greater accuracy in interpreting contextualized language,” a writes Zhou. “As proposed by Hazarika et al (2018), CASCADE is a contextual model that produces good results for detecting sarcasm. This study analyzes a Reddit corpus using these two state-of-the-art models and evaluates their performance compared to models basics to find the ideal approach to detecting sarcasm.

Essentially, Zhou performed a series of tests aimed at evaluating the ability of the CASCADE and RCNN-RoBERTa models to detect sarcasm in comments posted on Reddit, the popular online platform typically used to rate content and discuss various topics. The ability of these two models to detect sarcasm in sample texts was also compared to average human performance on this same task (reported in previous work) and to the performance of some basic models for analyzing texts.

“We found that contextual information, such as incorporating user personality, could significantly improve performance, as well as incorporating a RoBERTa transformer, compared to a more traditional CNN approach,” he said. Zhou concluded in his article. “Given the success of contextual and transformer-based approaches, as shown in our results, augmenting a transformer with additional contextual information features may be an avenue for future experiments.”

The results collected in this recent study may soon guide other studies in this area, ultimately contributing to the development of more effective LLMs for detecting sarcasm and irony in human language. These models could eventually prove to be extremely valuable tools for quickly performing sentiment analyzes on reviews, posts, and other user-generated content online.

© 2023 Science X Network