Can large language models detect sarcasm?

This promising class of natural language processing (NLP) models has become increasingly popular following the release of Open AI's ChatGPT platform, which can quickly answer a wide range of user questions and generate compelling written texts for various purposes.

As these models become more widespread, evaluating their capabilities and limitations is critically important. These evaluations can ultimately help understand situations in which LLMs are more or less useful while identifying ways in which they can be improved.

Juliann Zhou, a researcher at New York University, recently conducted a study to evaluate the performance of two LLMs trained to recognize human sarcasm, which involves conveying ideas by ironically expressing the exact opposite of what one means. means. Her findings, published on the arXiv preprint server, helped her define features and algorithmic components that could improve the sarcasm detection capabilities of AI agents and bots.

“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.

“Since the use of sarcasm is often context-based, previous research has used linguistic representation models such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) to identify sarcasm with information contextual. Recent innovations in NLP have provided more ways to detect 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. However, many reviews and comments posted online contain irony and sarcasm, which could lead models to label themselves as "positive" when they are expressing negative emotions, or vice versa.

Some computer scientists are therefore trying to develop models capable of detecting sarcasm in written text. Two of the most promising of these models, CASCADE and RCNN-RoBERTa, were presented by various research groups in 2018.

“Jacob Devlin et al. (2018) introduced a new language representation model and demonstrated higher accuracy in interpreting contextualized language in BERT: pre-training deep bidirectional transformers for language understanding,” Zhou wrote. “As stated by Hazarika et al. (2018), CASCADE is a contextual model that produces good results in sarcasm detection.

This study analyzes a Reddit corpus using these two state-of-the-art models and evaluates their performance against baseline models to find the ideal sarcasm detection approach."

Essentially, Zhou conducted a series of tests to evaluate the ability of the CASCADE and RCNN-RoBERTa models to detect sarcasm in comments posted on Reddit, the popular online platform usually used to rate content and discuss various topics.

The ability of these two models to detect sarcasm in the sample texts was also compared to average human performance on the same task (reported in previous work) and to the performance of some basic text analysis models.

“We found contextual insights, such as: “For example, integrating user personality, performance, and incorporating a transformational RoBERTa can significantly improve over a more traditional CNN approach ", Zhou concluded in his article. “Given the success of contextual and transformer-based approaches, as shown in our results, extending a transformer to include additional contextual information capabilities could be an avenue for future experimentation.”

The results collected in this current study may soon serve as a guide for other studies in this area and ultimately contribute to the development of LLMs capable of better-detecting sarcasm and irony in human language. These models could ultimately prove to be extremely valuable tools for quickly performing sentiment analysis of reviews, posts, and other user-generated content online.