Recent advancement in large language models (LLMs) has offered a strong
potential for natural language systems to process informal language. A
representative form of informal language is slang, used commonly in daily
conversations and online social media. To date, slang has not been
comprehensively evaluated in LLMs due partly to the absence of a carefully
designed and publicly accessible benchmark. Using movie subtitles, we construct
a dataset that supports evaluation on a diverse set of tasks pertaining to
automatic processing of slang. For both evaluation and finetuning, we show the
effectiveness of our dataset on two core applications: 1) slang detection, and
2) identification of regional and historical sources of slang from natural
sentences. We also show how our dataset can be used to probe the output
distributions of LLMs for interpretive insights. We find that while LLMs such
as GPT-4 achieve good performance in a zero-shot setting, smaller BERT-like
models finetuned on our dataset achieve comparable performance. Furthermore, we
show that our dataset enables finetuning of LLMs such as GPT-3.5 that achieve
substantially better performance than strong zero-shot baselines. Our work
offers a comprehensive evaluation and a high-quality benchmark on English slang
based on the OpenSubtitles corpus, serving both as a publicly accessible
resource and a platform for applying tools for informal language processing.

通过影视字幕构建了一个用于评估俚语处理的数据集，展示了该数据集在俚语检测和识别地域和历史俚语来源方面的有效性，同时揭示了大型语言模型在该数据集上的性能和微调的潜力。

走向非正式语言处理：大型语言模型中的俚语知识

Toward Informal Language Processing: Knowledge of Slang in Large  Language Models

Informal language is a style of spoken or written language frequently used in
casual conversations, social media, weblogs, emails and text messages. In
informal writing, the language faces some lexical and/or syntactic changes
varying among different languages. Persian is one of the languages with many
differences between its formal and informal styles of writing, thus developing
informal language processing tools for this language seems necessary. Such a
converter needs a large aligned parallel corpus of colloquial-formal sentences
which can be useful for linguists to extract a regulated grammar and
orthography for colloquial Persian as is done for the formal language. In this
paper we explain our methodology in building a parallel corpus of 50,000
sentence pairs with alignments in the word/phrase level. The sentences were
attempted to cover almost all kinds of lexical and syntactic changes between
informal and formal Persian, therefore both methods of exploring and collecting
from the different resources of informal scripts and following the phonological
and morphological patterns of changes were applied to find as much instances as
possible. The resulting corpus has about 530,000 alignments and a dictionary
containing 49,397 word and phrase pairs.

构建一个包含 50,000 个句对的并行语料库，用来提取波斯语非正式语言的规范文法和字词拼写变化。

制定一部波斯语非正式 - 正式语料库

Developing an Informal-Formal Persian Corpus

This study aimed to investigate the influence of the presence of informal
language, such as emoticons and slang, on the performance of sentiment analysis
models applied to social media text. A convolutional neural network (CNN) model
was developed and trained on three datasets: a sarcasm dataset, a sentiment
dataset, and an emoticon dataset. The model architecture was held constant for
all experiments and the model was trained on 80% of the data and tested on 20%.
The results revealed that the model achieved an accuracy of 96.47% on the
sarcasm dataset, with the lowest accuracy for class 1. On the sentiment
dataset, the model achieved an accuracy of 95.28%. The amalgamation of sarcasm
and sentiment datasets improved the accuracy of the model to 95.1%, and the
addition of emoticon dataset has a slight positive impact on the accuracy of
the model to 95.37%. The study suggests that the presence of informal language
has a restricted impact on the performance of sentiment analysis models applied
to social media text. However, the inclusion of emoticon data to the model can
enhance the accuracy slightly.

研究发现，社交媒体文本中使用表情符号和俚语等非正式语言对情感分析模型的表现具有有限的影响，但将表情符号数据整合到模型中可以稍微提高准确性。