Search papers, labs, and topics across Lattice.
This review paper surveys the applications of large language models (LLMs) in NLP tasks, focusing on the period between 2023 and 2025. It covers tasks such as information retrieval, named entity recognition, text classification, summarization, machine translation, question answering, and hate speech detection. The paper also demonstrates the calculation of evaluation metrics like ROUGE, BERTScore, METEOR, BARTScore, and BLEU using ChatGPT 3.5 generated text.
Forget benchmarks, this review shows how LLMs are being applied *right now* across ten crucial NLP tasks, from sniffing out fake news to powering machine translation.
The review enumerates the predominant applications of large language models (LLMs) in natural language processing (NLP) tasks, with a particular emphasis on the years 2023 to 2025. A particular emphasis is placed on applications pertaining to information retrieval, named entity recognition, text or document classification, text summarization, machine translation, question-and-answer generation, fake news or hate speech detection, and sentiment analysis of text. Furthermore, metrics such as ROUGE, BERT, METEOR, BART, and BLEU scores are presented to evaluate the capabilities of a given language model. The following example illustrates the calculation of scores for the aforementioned metrics, utilizing sentences generated by ChatGPT 3.5, which is free and publicly available.
