Toward Informal Language Processing: Knowledge of Slang in Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have offered a strong potential for natural language systems to process informal language. |
| Approach: | They propose to use movie subtitles to evaluate slang in large language models . they find that smaller LLMs finetuned on the dataset achieve comparable performance . |
| Outcome: | The proposed dataset can be used to evaluate LLMs on slang detection and identification of regional and historical sources for interpretive insights. |
Similar Papers
How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Slang is a commonly used type of informal language that poses a daunting challenge to NLP systems. |
| Approach: | They compare human-attested slang and swiss-generated slurs with machine-generated ones . they find that LLMs have significant knowledge about the creative aspects of sling . |
| Outcome: | The proposed model compares human and machine-generated slang usages to find biases in human perceptions of sling . the results suggest that human-attested slms have significant knowledge about the creative aspects of a language . |
SLANG: New Concept Comprehension of Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Dynamic nature of language limits the adaptability of Large Language Models (LLMs) Traditionally, LLMs are trained on static data, which limits their adaptability . |
| Approach: | They propose a benchmark to integrate novel data and assess LLMs’ ability to comprehend emerging concepts, alongside a causal inference-based approach to enhance LLM comprehension of new phrases and their colloquial context. |
| Outcome: | The proposed model outperforms baseline models in terms of precision and relevance in the comprehension of Internet slang and memes. |
Simple Models for Word Formation in Slang (N18-1)
Copied to clipboard
| Challenge: | slang is a popular vocabulary among young people due to its extragrammatical properties and the rise of social media. |
| Approach: | They propose a data-driven approach coupled with linguistic knowledge to develop generative models for three types of extra-grammatical word formation phenomena abounding in slang: Blends, Clippings, and Reduplicatives. |
| Outcome: | The proposed models show that slang exhibits extragrammatical properties that distinguish it from the standard form. |
A Computational Framework for Slang Generation (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing language models trained on large text corpora are biased toward formal language and under-represent slang. |
| Approach: | They propose a framework that models the speaker’s word choice in slang context by relating the conventional and sexist senses of a word while incorporating syntactic and contextual knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art language models and better predicts the historical emergence of slang word usages from 1960s to 2000s. |
“You Are An Expert Linguistic Annotator”: Limits of LLMs as Analyzers of Abstract Meaning Representation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) demonstrate proficiency and fluency in the use of language, but do they have the linguistic knowledge to serve as an expert linguistic annotator? |
| Approach: | They examine the successes and limitations of large language models using the Abstract Meaning Representation (AMR) parsing formalism. |
| Outcome: | The proposed models can reproduce the basic format of AMR, as well as some core event, argument, and modifier structure, but they have virtually no fully accurate parses. |
Understanding Slang with LLMs: Modelling Cross-Cultural Nuances through Paraphrasing (2024.emnlp-main)
Copied to clipboard
| Challenge: | a recent study examines the ability of large language models (LLMs) to paraphrase slang within climate-related tweets . slanted tweets from non-anglocentric countries may contain cultural references and idioms based on sociocultural identities . |
| Approach: | They investigate the ability of large language models to paraphrase slang within climate-related tweets from Nigeria and the UK. |
| Outcome: | The proposed model can paraphrase slang within climate-related tweets from Nigeria and the UK . the model can only parse sexist and sex-related slurs, the study shows . |
CUTE: Measuring LLMs’ Understanding of Their Tokens (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) perform well on a wide variety of tasks, authors say . they lack direct access to characters, which can be difficult to generalize to new languages . |
| Approach: | They propose a benchmark to test the orthographic knowledge of Large Language Models . they find that most LLMs seem to know the spelling of their tokens - yet fail to manipulate text . |
| Outcome: | The proposed benchmark tests the orthographic knowledge of large language models . it finds that most LLMs seem to know the spelling of their tokens, but fail to manipulate text . |
SLM-Mod: Small Language Models Surpass LLMs at Content Moderation (2025.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are expensive to query in real-time and do not allow for a community-specific approach to content moderation. |
| Approach: | They propose to use small language models for community-specific content moderation tasks by fine-tuning and evaluating their performance against larger open- and closed-sourced models. |
| Outcome: | The proposed models outperform zero-shot LLMs in content moderation tasks with 11.5% higher accuracy and 25.7% higher recall across all communities. |
Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies on LMs have focused on linguistic generalizations and representations from developmentally plausible data. |
| Approach: | They propose to use phoneme- and grapheme-based language models to learn linguistic units at and below the word level. |
| Outcome: | The proposed models can achieve strong performance on syntactic and novel benchmarks and match grapheme-based models in standard tasks and novel evaluations. |
Lexical Semantics with Large Language Models: A Case Study of English “break” (2023.findings-eacl)
Copied to clipboard
| Challenge: | Large neural language models (LLMs) can be powerful tools for research in lexical semantics. |
| Approach: | They argue that large neural language models can be powerful tools for research in lexical semantics by capturing known sense distinctions and identifying informative new sense combinations. |
| Outcome: | The proposed models capture many of the sense distinctions found in the English verb break and can be used to identify informative new sense combinations for further analysis. |