ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models
- URL: http://arxiv.org/abs/2503.17403v1
- Date: Wed, 19 Mar 2025 22:55:08 GMT
- Title: ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models
- Authors: Azim Akhtarshenas, Afshin Dini, Navid Ayoobi,
- Abstract summary: Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications.<n>This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training processes, and functionalities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications. This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training processes, and functionalities. We examine its integration into various domains across industries such as customer service, education, healthcare, and entertainment. A comparative analysis with other LLMs highlights ChatGPT's unique features and performance metrics. Regarding benchmarks, the paper examines ChatGPT's comparative performance against other LLMs and discusses potential risks such as misinformation, bias, and data privacy concerns. Additionally, we offer a number of figures and tables that outline the backdrop of the discussion, the main ideas of the article, the numerous LLM models, a thorough list of datasets used for pre-training, fine-tuning, and evaluation, as well as particular LLM applications with pertinent references. Finally, we identify future research directions and technological advancements, underscoring the evolving landscape of LLMs and their profound impact on artificial intelligence Artificial Intelligence (AI) and society.
Related papers
- Personalized Multimodal Large Language Models: A Survey [127.9521218125761]
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities.
This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications.
arXiv Detail & Related papers (2024-12-03T03:59:03Z) - Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions [2.5179515260542544]
Large Language Models (LLMs) have gained significant attention across academia and industry for their versatile applications in text generation, question answering, and text summarization.
To quantify the performance, it's crucial to have a comprehensive grasp of existing metrics.
This paper offers a comprehensive exploration of LLM evaluation from a metrics perspective, providing insights into the selection and interpretation of metrics currently in use.
arXiv Detail & Related papers (2024-04-14T03:54:00Z) - Characteristic AI Agents via Large Language Models [40.10858767752735]
This research focuses on investigating the performance of Large Language Models in constructing characteristic AI agents.
A dataset called Character100'' is built for this benchmark, comprising the most-visited people on Wikipedia for language models to role-play.
The experimental results underscore the potential directions for further improvement in the capabilities of LLMs in constructing characteristic AI agents.
arXiv Detail & Related papers (2024-03-19T02:25:29Z) - Large Language Models: A Survey [66.39828929831017]
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks.<n>LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
arXiv Detail & Related papers (2024-02-09T05:37:09Z) - Exploring the Potential of Large Language Models in Computational Argumentation [54.85665903448207]
Large language models (LLMs) have demonstrated impressive capabilities in understanding context and generating natural language.
This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models, and LLaMA2 models, in both zero-shot and few-shot settings.
arXiv Detail & Related papers (2023-11-15T15:12:15Z) - Aligning Large Language Models with Human: A Survey [53.6014921995006]
Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks.
Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information.
This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
arXiv Detail & Related papers (2023-07-24T17:44:58Z) - Several categories of Large Language Models (LLMs): A Short Survey [3.73538163699716]
Large Language Models(LLMs)have become effective tools for natural language processing and have been used in many different fields.
The survey emphasizes recent developments and efforts made for various LLM kinds, including task-based financial LLMs, multilingual language LLMs, biomedical and clinical LLMs, vision language LLMs, and code language models.
arXiv Detail & Related papers (2023-07-05T18:18:23Z) - Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks.
We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
arXiv Detail & Related papers (2023-05-24T10:45:25Z) - Document-Level Machine Translation with Large Language Models [91.03359121149595]
Large language models (LLMs) can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks.
This paper provides an in-depth evaluation of LLMs' ability on discourse modeling.
arXiv Detail & Related papers (2023-04-05T03:49:06Z)
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.