semvec/pypi-packages huggingface dataset:
ArXixLatestArticle 0.0.0.2
Search for updated article on arXiv.org
Requires Python: >=3.7
Keywords: python,pandas,numpy,request,PyPDF2


# Arvixgpt

## Step 1:

run the python script ArXixLatestArticle.py

```python
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:

```text
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:

````text

## 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')]
````