Evaluating Large Language Models' Knowledge of Slang and Its Implications for Informal Language Processing

Core Concepts

Large language models like GPT-4 contain considerable knowledge about slang usage, but task-specific finetuning is still essential for achieving state-of-the-art performance on slang processing tasks. The models' ability to identify the demographic sources of slang also raises potential privacy concerns.

Abstract

The paper investigates the knowledge of slang in large language models (LLMs) like GPT and BERT-like models. It makes the following key points:

Slang is a common form of informal language that is ubiquitous in daily conversations and online interactions, but has not been comprehensively evaluated in LLMs.

The authors contribute a new dataset called OpenSub-Slang, which contains thousands of human-annotated English slang usages from movie subtitles, along with their literal paraphrases and metadata on the regional and historical sources of the slang.

Using this dataset, the authors evaluate the LLMs on two core tasks: (1) slang detection, where the models need to identify the presence of slang in natural sentences, and (2) slang source identification, where the models need to classify the regional and temporal origins of the slang.

The results show that while larger GPT models outperform BERT-like models in both tasks, finetuning is still essential for achieving state-of-the-art performance. The GPT models also exhibit the ability to identify the demographic sources of slang, which raises potential privacy concerns.

The authors also find that the LLMs tend to be less confident on slang usages from the UK and more contemporary slang, likely due to biases in the training data.

Further analysis suggests that the LLMs, including GPT-4, do not seem to have encoded structural semantic knowledge about slang, but rather treat slang as additional "conventional" word senses learned from the training data.

Stats

"Good choice, that jacket is blazing." (Slang usage)
"Good choice, that jacket is excellent." (Literal paraphrase)
"We can't keep doing this sh⋆t, Charlie." (Slang usage)
"Knock it off." (Literal paraphrase)

Quotes

"Knowledge of slang in LLMs has important implications beyond automated processing of informal language. This is the case because the use of slang explicitly reflects one's social identity (Labov, 1972, 2006; Eble, 2012)."
"Given slang's close ties with social identity, a competent language model may also accurately reveal a slang user's identity. While such information can be used to improve NLP performance (Volkova et al., 2013; Hovy, 2015), the use of slang may also lead to an increased risk of personal information exposure."

Key Insights Distilled From

Toward Informal Language Processing

by Zhewei Sun,Q... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02323.pdf

Deeper Inquiries

How can the knowledge of slang in LLMs be leveraged to improve the performance of NLP systems across diverse demographic groups?

The knowledge of slang in Large Language Models (LLMs) can be leveraged to enhance the performance of Natural Language Processing (NLP) systems across diverse demographic groups in several ways:

Improved Understanding of Social Identity: Slang is closely tied to social identity, including factors like age, region, ethnicity, and community. By incorporating slang knowledge into NLP systems, these models can better understand the nuances of language use within different demographic groups.

Enhanced Contextual Understanding: Slang often carries contextual information that may not be apparent in literal language. LLMs that are trained to recognize and interpret slang can provide more accurate and contextually relevant responses in conversations with diverse demographic groups.

Personalization and Adaptation: By recognizing slang specific to different demographic groups, NLP systems can personalize their responses and adapt their language to better resonate with users from various backgrounds. This can lead to more engaging and effective communication.

Fairness and Inclusivity: Understanding slang from diverse demographic groups can help NLP systems avoid biases and ensure fair treatment of all users. By incorporating slang knowledge, these systems can provide more inclusive and respectful interactions.

Improved User Experience: Leveraging slang knowledge can enhance the user experience by making conversations more natural, relatable, and engaging for individuals from different demographic backgrounds.

In essence, by integrating slang knowledge into LLMs, NLP systems can become more culturally aware, linguistically diverse, and better equipped to cater to the needs of a wide range of users.

What are the potential ethical concerns and privacy implications of LLMs' ability to identify the demographic sources of slang, and how can these be addressed?

The ability of Large Language Models (LLMs) to identify the demographic sources of slang raises several ethical concerns and privacy implications:

Privacy Risks: Identifying the demographic sources of slang may inadvertently reveal personal information about individuals, including their age, region, or cultural background. This could lead to privacy breaches and expose sensitive details without users' consent.

Bias and Discrimination: If not handled carefully, the identification of demographic sources of slang could perpetuate biases or stereotypes against certain groups. This may result in discriminatory outcomes or reinforce existing societal inequalities.

Informed Consent: Users may not be aware that their demographic information is being inferred or used by NLP systems. Ensuring transparency and obtaining informed consent regarding the collection and utilization of such data is crucial to protect user privacy.

Data Security: Storing and processing demographic information in relation to slang usage poses data security risks. Safeguards must be implemented to protect this sensitive data from unauthorized access or misuse.

Mitigating Harm: NLP developers and organizations should proactively assess the potential harm that could arise from identifying demographic sources of slang. Implementing measures to mitigate these risks, such as anonymizing data or limiting the use of demographic information, can help address these concerns.

To address these ethical concerns and privacy implications, it is essential to prioritize user privacy, data protection, transparency, and fairness in the development and deployment of NLP systems that leverage demographic information related to slang.

What other forms of informal language, beyond slang, should be investigated to further understand the capabilities and limitations of LLMs in processing natural conversations?

In addition to slang, investigating other forms of informal language can provide valuable insights into the capabilities and limitations of Large Language Models (LLMs) in processing natural conversations. Some other forms of informal language that warrant investigation include:

Colloquialisms: Colloquial language, characterized by informal expressions and idiomatic phrases specific to certain regions or communities, can pose challenges for LLMs in understanding and generating contextually appropriate responses.

Jargon and Technical Terms: Domain-specific jargon and technical terms used in specialized fields or industries may require LLMs to have domain knowledge to accurately interpret and generate text in those contexts.

Emoticons and Emoji: The use of emoticons, emojis, and other visual elements in text communication adds layers of meaning and emotion that LLMs need to be able to interpret and incorporate into their responses.

Abbreviations and Acronyms: Understanding and disambiguating abbreviations and acronyms commonly used in informal communication, such as "LOL" or "BRB," is essential for LLMs to accurately capture the intended meaning.

Sarcasm and Irony: Detecting and interpreting sarcasm, irony, and other forms of figurative language is crucial for LLMs to grasp the true intent behind a statement and generate contextually appropriate responses.

By exploring these various forms of informal language, researchers can gain a more comprehensive understanding of the linguistic challenges faced by LLMs in processing natural conversations and develop more robust models that can effectively navigate the complexities of informal communication.