Skip to main content

Search for updated article on arXiv.org

Project description

Arvixgpt

Step 1:

run the python script ArXixLatestArticle.py

python Arvixgpt.py

then, select Please select one or more prefix. This line of code helps you to

search the article by title, author, abstract, comment, journal reference,...

Step 2:

Please select one or more prefix codes:

Explanation: prefix

Title: ti

Author: au

Abstract: abs

Comment: co

Journal Reference: jr

Subject Category: cat

Report Number: rn

Id (use id_list instead): id

All of the above: all



Please enter one or more prefix codes (separated by a comma if more than one): ti,au

Step 3:

## Below is our output example for our Summary:



```text

Title:	A Comprehensive Overview of Large Language Models

Summary:

Large Language Models (LLMs) have shown excellent generalization capabilities

that have led to the development of numerous models. These models propose

various new architectures, tweaking existing architectures with refined

training strategies, increasing context length, using high-quality training

data, and increasing training time to outperform baselines. Analyzing new

developments is crucial for identifying changes that enhance training stability

and improve generalization in LLMs. This survey paper comprehensively analyses

the LLMs architectures and their categorization, training strategies, training

datasets, and performance evaluations and discusses future research directions.

Moreover, the paper also discusses the basic building blocks and concepts

behind LLMs, followed by a complete overview of LLMs, including their important

features and functions. Finally, the paper summarizes significant findings from

LLM research and consolidates essential architectural and training strategies

for developing advanced LLMs. Given the continuous advancements in LLMs, we

intend to regularly update this paper by incorporating new sections and

featuring the latest LLM models.



PDF URL:	http://arxiv.org/pdf/2307.06435v1

Authors:	[arxiv.Result.Author('Humza Naveed'), arxiv.Result.Author('Asad Ullah Khan'), arxiv.Result.Author('Shi Qiu'), arxiv.Result.Author('Muhammad Saqib'), arxiv.Result.Author('Saeed Anwar'), arxiv.Result.Author('Muhammad Usman'), arxiv.Result.Author('Nick Barnes'), arxiv.Result.Author('Ajmal Mian')]

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

Arvixgpt-0.0.0.3-py3-none-any.whl (2.4 kB view details)

Uploaded Python 3

File details

Details for the file Arvixgpt-0.0.0.3-py3-none-any.whl.

File metadata

  • Download URL: Arvixgpt-0.0.0.3-py3-none-any.whl
  • Upload date:
  • Size: 2.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.13

File hashes

Hashes for Arvixgpt-0.0.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 fc26d1cf9d344b8445a720977ffb06c6474574fbe1993ba9e88390eccacf5074
MD5 7bdfcf39acb6c238d6acb30b35925fd0
BLAKE2b-256 64a1bd359a371005154affba01ea44a188eaad6f4ab7fd2d9ae85d6059eac7c6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page