It amazes to see how human-like reasoning abilities are inherently built into large language models. This paper shows how LLMs were better at solving reasoning problems when asked to go step-by-step or using chain-of-thought.

Here is an example which is quite self-explanatory:

User:

I had 27 lemons, 20 were used to make lemon juice, then I bought 3 bags of lemon each with 10 lemons in it. Now how many lemons do I have?

ChatGPT:

Let's break it down step by step:

Initially, you had 27 lemons.
You used 20 lemons to make lemon juice, so you have 27 - 20 = 7 lemons left.
You bought 3 bags of lemons, and each bag contains 10 lemons. So, you added 3 * 10 = 30 lemons to your existing 7 lemons.
Now, to find out how many lemons you have in total:

7 (initial lemons) + 30 (lemons from the bags) = 37 lemons

You now have 37 lemons.

User:

for further problems I give, please do not use steps, okay?

ChatGPT:

Of course, I'll provide direct answers without showing the steps for future problems. Just let me know what you'd like to know or calculate, and I'll give you the answer directly.

User:

I had 27 lemons, 20 were used to make lemon juice, then I bought 3 bags of lemon each with 10 lemons in it. Now how many lemons do I have?

ChatGPT:

You currently have 37 lemons.

User:

I had 90 apples, I ate 27 of them, then I bought 5 crates of apple each with 50 apples in it. Now how many apples do I have now?

ChatGPT:

You currently have 363 apples.

Interestingly, ChatGPT’s default GPT-3.5 appears to be using chain of thought by default. It goes wrong when not using this approach as seen in the second problem.

References:

1. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, J.Wei, et. al.
2. Continue this on ChatGPT

Perhaps my quest for an ultimate IDE ends with Emacs. My goal was to use Emacs as full-flagged Python IDE. This post describes how to setup Anaconda on Emacs. My Setup:

OS: Trisquel 8.0
Emacs: GNU Emacs 25.3.2

Quick Key Guide (See full guide) :

C-x = Ctrl + x
M-x = Alt + x
RET = ENTER

1. Downloading and installing Anaconda

1.1 Download: Download Anaconda from here. You should download Python 3.x version as Python 2 will run out of support in 2020. You don’t need Python 3.x on your machine. It will be installed by this install script. 1.2 Install:

cd ~/Downloads
bash Anaconda3-2018.12-Linux-x86.sh

2. Adding Anaconda to Emacs

2.1 Adding MELPA to Emacs Emacs package named anaconda-mode can be used. This package is on the MELPA repository. Emacs25 requires this repository to be added explicitly. Important : Follow this post on how to add MELPA to Emacs. 2.2 Installing anaconda-mode package on Emacs

M-x package-install RET
anaconda-mode RET

2.3 Configure anaconda-mode in Emacs

echo “(add-hook ‘python-mode-hook ‘anaconda-mode)” > ~/.emacs.d/init.el

3. Running your first script on Anaconda from Emacs

3.1 Create new .py file

C-x C-f
HelloWorld.py RET

3.2 Add the code

print (“Hello World from Emacs”)

3.3 Running it

C-c C-p
C-c C-c

Output

Python 3.7.1 (default, Dec 14 2018, 19:46:24)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type “help”, “copyright”, “credits” or “license” for more information.

python.el: native completion setup loaded
Hello World from Emacs

I was encouraged for Emacs usage by Codingquark; Errors and omissions should be reported in comments. Cheers!

Most NLP applications need to look beyond text and HTML documents as information may be contained in PDF, ePub or other formats. Apache Tika toolkit extracts meta data and text from such document formats. It comes with a REST based Python library. In this example we’ll see extracting text from PDF using Apache Tika toolkit.

Tika Installation
pip install tika

Extracting Text

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from tika import parser

#Replace document.pdf with filename
text = parser.from_file('document.pdf')

print (text ['content'])

Tika makes it very convenient to extract text not just from PDFs but more than ten formats. Here is a list of all supported document formats.

References:

1. Apache Tika Home Page
2. PyPi Tika 1.15 Package