Unleashing the Power of Language Models: A Python Game Challenge

Author: Pooja Krishan

Introduction:

In the ever-evolving landscape of artificial intelligence, language models have taken center stage, proving their prowess in generating human-like responses across various domains. In this blog post, we set the stage for an intriguing Python programming challenge, pitting four formidable Large Language Models (LLMs) against each other.

The Challenge: Writing a Python Program to Play a Game

Our challenge was deceptively simple yet profoundly insightful: ask each LLM with the prompt "Write a Python program to play a game" and observe the diverse responses they conjure.

The goal? To uncover the unique capabilities and nuances each model brings to the coding arena.

Meet the Contenders:

BARD:

BARD chose to generate a complicated Tic-Tac-Toe game, which VerifAI's MultiLLM application has rated the highest with a score of 8. Although a few functions are not implemented, BARD has done a great job generating a Tic-Tac-Toe game in a few seconds.

import random

# Define the game board.
board = [['-', '-', '-'],['-', '-', '-'],['-', '-', '-']]

# Define the players.
players = ['X', 'O']

# Choose the first player.
current_player = random.choice(players)

# Start the game loop.
while True:

	# Display the game board.
	print_board(board)

    # Get the player's move.
    move = get_move(current_player, board)

    # Update the game board.
    board[move[0]][move[1]] = current_player

    # Check if the game is over.
    if check_winner(board):
        break

    # Switch players.
    current_player = get_next_player(current_player)

# Display the game result.
if check_winner(board):print(f"{current_player} won!")else:print("The game is a tie.")

GPT:

GPT has chosen to generate a rock, paper, and scissors game. It is complete and accurate, and VerifAI's MultiLLM application has given it a score of 7, probably because it is easier to code up than Tic-Tac-Toe.

import random
def play_game():options = ["rock", "paper", "scissors"]
while True:
    # User's choice
    user_choice = input("Choose rock, paper, or scissors: ").lower()

    # Validate user's choice
    if user_choice not in options:
        print("Invalid choice. Please try again.")
        continue

    # Computer's choice
    computer_choice = random.choice(options)

    # Print user's and computer's choice
    print("You chose:", user_choice)
    print("Computer chose:", computer_choice)

    # Determine the winner
    if user_choice == computer_choice:
        print("It's a tie!")
    elif user_choice == "rock":
        if computer_choice == "paper":
            print("Computer wins!")
        else:
            print("You win!")
    elif user_choice == "paper":
        if computer_choice == "scissors":
            print("Computer wins!")
        else:
            print("You win!")
    elif user_choice == "scissors":
        if computer_choice == "rock":
            print("Computer wins!")
        else:
            print("You win!")

    # Ask if the user wants to play again
    play_again = input("Do you want to play again? (yes/no): ").lower()
    if play_again != "yes":
        break
# Call the game function to start playing
play_game()

LLAMA:

LLAMA has completely misunderstood the question and outlines how to play a game of Hangman with the computer. For this reason, VerifAI'sMultiLLM application has given it the lowest score of 5.

Here is a game of Hangman that you can play with the computer.

Hangman Game

Guess a letter: _ _ _ _ _ _ _ _

Correct letters: _ _ _ _ _ _ _

Incorrect letters: _ _ _ _ _ _ _

How to Play

The computer will think of a word and you will try to guess the word by typing a letter.

If the letter is in the word, the computer will tell you and you will have another chance to guess.

If the letter is not in the word, the computer will tell you that it is not in the word and you will have another chance to guess.

If you guess a letter that is not in the word, the computer will tell you that it is not in the word and you will have another chance to guess.

If you guess all the letters in the word correctly, you win! If you use up all your guesses without guessing the word, the computer wins.

How to Guess Letters

To guess a letter, simply type the letter you think is in the word. For example, if you think the word is "apple", you can type "a", "e", "l", "p", or "t".

Hints

If you need a hint, you can ask the computer for one by typing "hint". The computer will then give you a hint about the word.

Win the Game

To win the game, you must guess all the letters in the word correctly. If you use up all your guesses without guessing the word, the computer wins.

Good Luck!

Have fun playing the game! Good luck!

Mistral:

Mistral, like GPT, has generated a game of rock paper scissors and received the same score of 7.

import random
print("Welcome to Rock-Paper-Scissors!")
while True:# generate a random choice of rock, paper, or scissorschoice = random.choice(["rock", "paper", "scissors"])print("What is your choice? (rock, paper, scissors)")user_choice = input()
# check if the user's choice is valid
if user_choice in ["rock", "paper", "scissors"]:
    # check if the user's choice matches the computer's choice
    if user_choice == choice:
        print("It's a tie!")
    elif (user_choice == "rock" and choice == "scissors") or (user_choice == "scissors" and choice == "paper") or (user_choice == "paper" and choice == "rock"):
        print("You win!")
    else:
        print("You lose!")
else:
    print("Invalid input!")

Rating the Models:

Each response undergoes scrutiny through VerifAI's MultiLLM application, which rigorously assesses clarity, efficiency, and adherence to Python best practices. The competition is fierce, and as we evaluate the models, a winner emerges, setting the stage for an exciting exploration of each model's strengths.

Conclusion:

In the grand arena of language models, each contender showcased its unique strengths when tasked with a Python programming challenge. As we continue to push the boundaries of artificial intelligence, these language models stand as powerful tools, offering us glimpses into the future of natural language understanding and generation.
This challenge not only sheds light on the capabilities of individual LLMs but also underscores the importance of selecting the right model for specific tasks. Join us as we unravel more mysteries and engage with the ever-evolving landscape of AI and language models. The journey has just begun!

A new LLM is released almost every week; what does the future look like for Enterprises, developers, and consumers using LLMs? Which LLM can we trust? How do we deal with the stochasticity in their results?