SLANG : New Concept Comprehension of Large Language Models (2024)

Abstract: The dynamic nature of language, particularly evident in the realm of slang and memes on the Internet, poses serious challenges to the adaptability of Large Language Models (LLMs). Traditionally anchored to static datasets, these models often struggle to keep up with the rapid linguistic evolution characteristic of online communities. This research aims to bridge this gap by enhancing LLMs’ comprehension of the evolving new concepts on the Internet, without the high cost of continual retraining. In pursuit of this goal, we introduce SLNAG , a benchmark designed to autonomously integrate novel data and assess LLMs’ ability to comprehend emerging concepts, alongside FOCUS , an approach uses causal inference to enhance LLMs to understand new phrases and their colloquial context. Our benchmark and approach involves understanding real-world instances of linguistic shifts, serving as contextual beacons, to form more precise and contextually relevant connections between newly emerging expressions and their meanings. The empirical analysis shows that our causal inference-based approach outperforms the baseline methods in terms of precision and relevance in the comprehension of Internet slang and memes.

## introduction recently, language evolution has been accelerated by the online community, which has introduced new dimensions to linguistic shifts @xcite @xcite . these rapid changes in language pose serious challenges to the large language models (llms) on understanding the newly emerging concepts @xcite . generally, llms are trained on static data @xcite , which limits their adaptivity to the dynamic and ever-evolving nature of human language. this limitation is particularly pronounced in the context of digital communication, where new forms of expression and concepts emerge at an unprecedented pace @xcite . hence, it is essential for llms to understand linguistic shifts and new concepts without constant updates or external data. moreover, llms often make decisions based on superficial patterns rather than justified reasons. this can hinder their ability to accurately interpret and follow human instructions, as highlighted in several studies @xcite @xcite . for example, as depicted in figure 1 , the chain-of-thought (cot) prompting @xcite simply interprets the phrase the sunset is beautiful, isn't it? and misses the deeper, metaphorical meaning, which could represent an acknowledgment of an ending, like concluding a life phase or a relationship, in a complex conversation. this situation underscores the importance of enhancing and evaluating llms in a way that goes beyond their performance metrics. it's essential to consider the fundamental principles that guide their decision-making processes. therefore, we propose slang (similarity of lexical analysis and grasp), a benchmark to assess language models' adaptability to linguistic shifts, and focus (factual context causal analysis), an approach based on causal inference for enhancing comprehension of new concepts. the slang benchmark, developed from urban-dictionary (urban dictionary llc), focuses on assessing the capability of language models to maintain coherence and accuracy in the face of dynamic and unconventional language use, such as slang and idiomatic expressions, thereby evaluating the capability of llms in grasping new concepts. we select recent entries after a specific cutoff date and filter out phrases already likely in llm training data. we utilize user-generated ratings (upvotes and downvotes) to refine the dataset, ensuring its quality and comprehensiveness. the dataset is then standardized into a formal dictionary format, simplifying explanations and examples for universal understanding while retaining original meanings. this preprocessing approach ensures slang effectively evaluates llms' adaptability to linguistic shifts. focus employs causal inference to enhance models' comprehension of new concepts within evolving linguistic contexts. by analyzing causal relationships in language, focus advances models' predictive capabilities beyond traditional correlation-based learning. this method allows for a nuanced grasp of language dynamics, improving models' adaptability and effectiveness in applications requiring deep understanding of language use. focus significantly enhanced performance in language model comprehension, demonstrating superior precision and adaptability. with claude 3, focus achieved an f 1 score of 0.4596, precision of 0.4452, and recall of 0.4827, alongside an accuracy of 89.7%, outperforming previous methods in comprehension and adaptability. the codes for the slang and focus toolboxes are open-sourced, contributing to the community's resources for advancing language model development. ## slang we introduce slang in response to rapidly evolving language. slang benchmark evaluates llms' capability to interpret the dynamic landscape of user-generated new concepts. it uniquely features factual and counterfactual datasets, each crucial for gauging llm adaptability. we detail slang's dataset construction, and evaluation metrics in the following subsections. ## focus this section outlines the novel approach employed in our research to enhance the adaptability of llms in understanding the evolving human language. our method analogizes the dynamic nature of language to a continuously evolving entity that requires adaptive comprehension strategies. ## conclusion in this work, we have explored the dynamic and evolving nature of internet language, particularly slang and memes, and their impact on the adaptability of llms. our study introduced a novel benchmark, slang, to assess llms' proficiency in comprehending emerging linguistic trends. additionally, we proposed the focus methodology, which utilizes causal inference to enhance understanding of new concepts, going beyond other methods in terms of precision and relevance. our approach involves the construction of datasets from urban-dictionary, a platform known for user-generated content that reflects current language trends. we incorporated both factual and counterfactual instances to provide diverse linguistic contexts. factual instances are drawn directly from the urban-dictionary entries, while counterfactual instances are created by altering real-world examples to assess the models' adaptability to hypothetical scenarios. the results from our experiments demonstrate the enhanced capability of llms, equipped with our focus method, to adapt to the rapid evolution of online language. this research contributes to the field of natural language processing by emphasizing the importance of contextual understanding and adaptability in llms. our findings suggest that llms can effectively navigate the complexities of evolving human communication when equipped with robust methodologies like focus and evaluated against benchmarks such as slang.