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

• URL: http://arxiv.org/abs/2404.02323v2
• Date: Sat, 13 Apr 2024 02:17:01 GMT
• Title: Toward Informal Language Processing: Knowledge of Slang in Large Language Models
• Authors: Zhewei Sun, Qian Hu, Rahul Gupta, Richard Zemel, Yang Xu,
• Abstract summary: 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. 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.
• Score: 16.42982896928428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Generating bilingual example sentences with large language models as lexicography assistants [2.6550899846546527]
  We present a study of LLMs' performance in generating and rating example sentences for bilingual dictionaries across languages with varying resource levels. We evaluate the quality of LLM-generated examples against the GDEX (Good Dictionary EXample) criteria: typicality, informativeness, and intelligibility.
  arXiv  Detail & Related papers  (2024-10-04T06:45:48Z)
• LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study [2.8948274245812327]
  Grapheme-to-phoneme (G2P) conversion is critical in speech processing. Large language models (LLMs) have recently demonstrated significant potential in various language tasks. We present a benchmarking dataset designed to assess G2P performance on sentence-level phonetic challenges of the Persian language.
  arXiv  Detail & Related papers  (2024-09-13T06:13:55Z)
• Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem [4.830018386227]
  This work investigates the in-context learning abilities of pretrained large language models (LLMs) when instructed to translate text from a low-resource language into a high-resource language as part of an automated machine translation pipeline. We conduct a set of experiments translating Southern Quechua to Spanish and examine the informativity of various types of context retrieved from a constrained database of digitized pedagogical materials and parallel corpora.
  arXiv  Detail & Related papers  (2024-06-21T20:02:22Z)
• TasTe: Teaching Large Language Models to Translate through Self-Reflection [82.83958470745381]
  Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. We propose the TasTe framework, which stands for translating through self-reflection. The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-12T17:21:21Z)
• High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models [5.632410663467911]
  We explore the extent to which pretrained large language models (LLMs) can bridge the performance gap for under-resourced languages. We find that LLMs easily set the state of the art for the under-resourced languages by substantial margins. For all our languages, human evaluation shows on-a-par performance with humans for our best systems, but BLEU scores collapse compared to English.
  arXiv  Detail & Related papers  (2024-02-19T16:29:40Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
  Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
  arXiv  Detail & Related papers  (2023-06-20T08:27:47Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages [87.5457337866383]
  We introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. We find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks.
  arXiv  Detail & Related papers  (2022-01-27T18:53:22Z)
• LaoPLM: Pre-trained Language Models for Lao [3.2146309563776416]
  Pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. Although PTMs have been widely used in most NLP applications, it is under-represented in Lao NLP research. We construct a text classification dataset to alleviate the resource-scare situation of the Lao language. We present the first transformer-based PTMs for Lao with four versions: BERT-small, BERT-base, ELECTRA-small and ELECTRA-base, and evaluate it over two downstream tasks: part-of-speech tagging and text classification.
  arXiv  Detail & Related papers  (2021-10-12T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.