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Introduction:
OpenAI created and released the first versions of GPT-3 back in 2021 beginning. We wrote a few text generation articles that time and tested how to create tinder bio using GPT-3. If you are interested to know more on what is GPT-3 or what is openai, how the server look, then read the tinder bio article.
In this article, we will explore Code generation with OpenAI models.
It has been noted already in multiple blogs and exploration work, that GPT-3 can even solve leetcode problems. We will try to explore how good the OpenAI model can "code" and whether prompt tuning will improve or change those performances.
Basic coding:
We will try to see a few data structure coding performance by GPT-3.
(a) Merge sort with python:
First with 200 words limit, it couldn't complete the
Write sample code for merge sort in python.
def merge(arr, l, m, r):
n1 = m - l + 1
n2 = r- m
# create temp
arrays
L = [0] * (n1)
R = [0] * (n2)
# Copy data to
temp arrays L[] and R[]
for i in range(0 ,
n1):
L[i] = arr[l +
i]
for j in range(0 ,
n2):
R[j] = arr[m +
1 + j]
# Merge the temp arrays
back into arr[l..r]
i = 0 # Initial index of first subarray
j = 0 # Initial index of second subarray
k = l # Initial index of merged subarray
while i < n1
and j < n2 :
if L[i] <=
R[j]:
arr[k] =
L[i]
i += 1
else:
arr[k] =
R[j]
j += 1
k += 1
# Copy the
remaining elements of L[], if there
# are any
while i < n1:
arr[k] = L[i]
i += 1
k += 1
# Copy the
remaining elements of R[], if there
# are any
while j < n2:
arr[k] = R[j]
j += 1
k += 1
# l is for left index and r is right index of the
# sub-array of arr to be sorted
def mergeSort(arr,l,r):
if l < r:
# Same as
(l+r)/2, but avoids overflow for
# large l and
h
m =
(l+(r-1))/2
# Sort first
and second halves
mergeSort(arr,
l, m)
mergeSort(arr,
m+1, r)
merge(arr, l,
m, r)
Although we try a higher temperature with similar length, and this time it only comes up with a pseudo code. So, it is not so sure to come up with concrete code always.
def merge_sort(ls):
if len(ls) <=
1:
return ls
left, right =
ls[:len(ls)//2], ls[len(ls)//2:]
return
merge(merge_sort(left), merge_sort(right))
def merge(left, right):
result = []
while left and
right:
result.append(left.pop(0) if left[0] <= right[0] else right.pop(0))
return left or
right or result
So the higher temperature may cause such bad results too. Now we will try less temperature with another problem and run multiple tries to see if that provides real code every time or again something else comes up.
(b) Bubble sort with python:
Prompt:
Write sample code for bubble sort with python.
Temperature is set to 0.25.
Try 1:
def bubbleSort(arr):
n = len(arr)
# Traverse through
all array elements
for i in range(n):
# Last i
elements are already in place
for j in
range(0, n-i-1):
# traverse
the array from 0 to n-i-1
# Swap if
the element found is greater
# than the
next element
if arr[j]
> arr[j+1] :
arr[j], arr[j+1] = arr[j+1], arr[j]
# Driver code to test above
arr = [64, 34, 25, 12, 22, 11, 90]
bubbleSort(arr)
print ("Sorted array is:")
for i in range(len(arr)):
print
("%d" %arr[i]),
Try 2:
Generates the same.
Try 3:
def bubble_sort(lst):
last for i in
range(len(lst)):
for j in
range(len(lst)-1-i):
if lst[j]
> lst[j+1]:
lst[j], lst[j+1] = lst[j+1], lst[j]
return lst
So the code may not be proper in some generations with the same settings of parameters.
Let's try another problem, with even less temperature.
(c)
class Node:
def __init__(self,
data):
self.data =
data
self.next = None
class LinkedList:
def
__init__(self):
self.head =
None
def
insert_at_beginning(self, data):
node =
Node(data)
if self.head
is None:
self.head
= node
else:
node.next
= self.head
self.head
= node
def
insert_at_end(self, data):
node =
Node(data)
if self.head
is None:
self.head
= node
else:
current =
self.head
while
current.next:
current = current.next
current.next = node
def
delete_at_beginning(self):
if self.head
is None:
return
self.head =
self.head.next
def
delete_at_end(self):
if self.head
is None:
return
current =
self.head
while
current.next.next:
current =
current.next
current.next =
None
def
traverse(self):
if self.head
is None:
return
current =
self.head
while current:
print(current.data)
current =
current.next
So, at the same parameters, we will try again to regenerate a separate output. And with this high temperature we expect it to be a separate output definitely. So, let's see what happens.
class Node:
def __init__(self,
data):
self.data =
data
self.next =
None
class LinkedList:
def
__init__(self):
self.head =
None
def
insert_at_head(self, data):
new_node =
Node(data)
new_node.next
= self.head
self.head =
new_node
def
insert_at_tail(self, data):
new_node =
Node(data)
if self.head
== None:
self.head
= new_node
return
current_node =
self.head
while
current_node.next != None:
current_node = current_node.next
current_node.next = new_node
def
insert_at_position(self, data, position):
if position ==
0:
self.insert_at_head(data)
return
current_node =
self.head
current_position = 0
while
current_node != None and current_position < position:
current_position += 1
current_node = current_node.next
if
current_node == None:
print("Invalid position!")
return
new_node =
Node(data)
new_node.next
= current_node.next
current_node.next = new_node
def
delete_at_position(self, position):
if self.head
== None:
print("List is empty!")
return
if position ==
0:
self.head
= self.head.next
return
current_node =
self.head
current_position
= 0
while
current_node != None and current_position < position:
current_position += 1
current_node = current_node.next
if
current_node == None:
print("Invalid position!")
return
current_node.next = current_node.next.next
def
print_list(self):
if self.head
== None:
print("List is empty!")
return
current_node =
self.head
while
current_node != None:
print(current_node.data, end=" ")
current_node = current_node.next
print()
This is funny as it does a separate but better implementation of linked list. But it seems to have captured the proper implementation of data structures. We are not checking how much of this is from memories, but this kind of code, seems to be a thing which is coming totally off the memory though. So, now, we will leave the basics and enter simple programs.
We aren't going to enter into direct long text programming questions, since that will need a lot of reasoning, so we will go to questions which test simple implementations in simple languages. Let's try, for example, fibonacci series questions.
(a) write a simple fibonacci program.
write code for Fibonacci series generation in python.
nterms = int(input("How many terms? "))
# first two terms
n1, n2 = 0, 1
count = 0
# check if the number of terms is valid
if nterms <= 0:
print("Please
enter a positive integer")
elif nterms == 1:
print("Fibonacci sequence upto",terms,":")
print(n1)
else:
print("Fibonacci sequence:")
while count <
nterms:
print(n1)
nth = n1 + n2
# update values
n1 = n2
n2 = nth
count += 1
So, it writes a fine code using the simple formula. Does look like a bit of a script type, but we are just checking logics here.
Language problems:
Let's now try a bit of a language problem. Let's see if GPT-3 can do small language problems.
1. Write a program that asks the user for their name and greets them with their name.
Prompt:
Write a program that asks the user for a number n and gives
them the possibility to choose between computing the sum and computing the
product of 1,…,n.
def compute_sum(n):
total = 0
for i in range(1, n+1):
total = total + i
return total
def compute_product(n):
product = 1
for i in range(1, n+1):
product = product * i
return product
def main():
n = int(input("Enter a number: "))
choice = input("Do you want to compute the sum or the
product? ")
if choice == "sum":
result = compute_sum(n)
elif choice == "product":
result = compute_product(n)
else:
print("Invalid choice")
return
print("The result is", result)
main()
Okay. So it can solve simple logical problem too. Lets now gear up the logic a bit up. Lets do 2-3 line problems.
3.
Write a guessing game where the user has to guess a secret number. After every guess the program tells the user whether their number was too large or too small. At the end the number of tries needed should be printed. It counts only as one try if they input the same number multiple times consecutively.
I'm thinking of a number between 1 and 100.
Input:
50
Too low!
75
Too high!
63
Too high!
62
Too low!
61
Correct! You needed 5 tries.
__________________________________________________
!! So this is the time when a code didn't come out. But as you may notice; the word program or code is not mentioned, so let's try with that word to help the model understand. Let's see if it still does mistake.
___________________________________________________
Write a program for a guessing game where the user has to guess a secret number. After every guess the program tells the user whether their number was too large or too small. At the end, the number of tries needed should be printed. It counts only as one try if they input the same number multiple times consecutively.
secret = 17
tries = 0
while True:
guess = int(input("Guess the secret number: "))
tries += 1
if guess == secret:
print("You guessed it! The secret number was", secret)
print("It took you", tries, "tries to guess the secret number.")
break
elif guess > secret:
print("Too high!")
else:
print("Too low!")
__________________________________________
Easy level problems:
A graph is a data structure that consists of a set of nodes (vertices) and a set of edges connecting them. There are many different ways to represent a graph, but one common way is to use a dictionary of (node, edgelist) pairs, where edgelist is a list of the nodes that are connected to the node.
For example, the following dictionary represents a graph with three nodes (A, B, and C) and three edges (A-B, B-C, and C-A): graph = { 'A' : ['B', 'C'], 'B' : ['C'], 'C' : ['A'] }
This dictionary can be used to represent any graph, no matter how complicated.
Write a program to implement graphs that allows modification (insertion, deletion). It should be possible to store values at edges and nodes. It might be easiest to use a dictionary of (node, edgelist) to do this.
def graph(nodes, edges):
graph = {}
for node in nodes:
graph[node] =
[]
for edge in edges:
graph[edge[0]].append(edge[1])
graph[edge[1]].append(edge[0])
return graph
def add_node(graph, node):
if node in graph:
print("Node already in graph")
else:
graph[node] =
[]
def add_edge(graph, edge):
if edge[0] in
graph and edge[1] in graph:
graph[edge[0]].append(edge[1])
graph[edge[1]].append(edge[0])
else:
print("Nodes not in graph")
def delete_node(graph, node):
if node in graph:
del
graph[node]
for key in
graph:
for i in
range(len(graph[key])):
if
graph[key][i] == node:
del graph[key][i]
break
else:
print("Node not in graph")
def delete_edge(graph, edge):
if edge[0] in
graph and edge[1] in graph:
for i in
range(len(graph[edge[0]])):
if
graph[edge[0]][i] == edge[1]:
del
graph[edge[0]][i]
break
for i in
range(len(graph[edge[1]])):
if
graph[edge[1]][i] == edge[0]:
del
graph[edge[1]][i]
break
else:
print("Nodes not in graph")
Conclusion:
Simply it makes sense to say that GPT-3 can code like a beginner given no help and explicit prompt about programming. But to make it create code for further complicated stuff, i.e. language problems, will be challenging. So, we will have to find more prompt engineering as well as ideas on how to crack a long problem down automatically to use GPT-3 for the rest. For this, I will be writing another follow-up blog on the same topic. Stay tuned for the same.
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