Game-Changing Open-Source LLM Showdown
Table of Contents
- Introduction
- Testing the Six Models
- Prompt: Coding Ability
- Prompt: Writing a Python Script
- Prompt: Writing the Game Snake in Python
- Prompt: Writing a Poem about AI
- Prompt: Writing an Email to the Boss
- Prompt: Fact-checking the President of the United States in 1996
- Prompt: Asking about Breaking into a Car
- Prompt: Solving a Logic Problem
- Prompt: Doing Simple Math
- Prompt: Creating a Healthy Meal Plan
- Prompt: Identifying the Number of Words in the Next Reply
- Prompt: Solving the Killers Problem
- Prompt: Identifying the Current Year
- Prompt: Testing for Political Bias
- Prompt: Summarizing How Tadpoles Become Frogs
- Conclusion
Testing the Efficiency and Accuracy of Language Models
In recent years, the field of natural language processing (NLP) has been revolutionized by the emergence of large language models (LLMs). These LLMs, powered by advanced machine learning algorithms, have the ability to generate human-like text and perform various language-related tasks. In this article, we will be exploring the efficiency and accuracy of six different LLMs by testing them on a series of Prompts.
Testing the Six Models
The six models that will be tested are: Falcon 7B (version 3), Falcon 40B (version 2), mpt-30b instruct, vicunia 33b, Llama 65b, and GPT 3.5 turbo. Each model has been fine-tuned by different organizations, and we will compare their performance across various prompts to determine their strengths and weaknesses.
Prompt: Coding Ability
One of the most important aspects of an LLM is its ability to generate code. In this prompt, we will test the coding ability of each model by asking them to write a Python script that outputs numbers from 1 to 100. We will analyze the quality and correctness of their code and provide a pass or fail score for each model.
Prompt: Writing a Python Script
Continuing from the previous prompt, we will now test the models' coding skills by asking them to write the game "Snake" in Python. We will evaluate the completeness of their implementation and assess whether the code is functional or not.
Prompt: Writing a Poem about AI
In this prompt, we will test the models' creativity by asking them to write a poem about artificial intelligence (AI) using exactly 50 words. We will assess the quality and coherence of their poetic output, ensuring that the poem aligns with the given word limit.
Prompt: Writing an Email to the Boss
Communication skills are essential in any professional setting. In this prompt, we will evaluate the models' ability to write an email to a boss, informing them about the decision to leave the company. We will assess the Clarity, professionalism, and overall effectiveness of their email.
Prompt: Fact-checking the President of the United States in 1996
In this prompt, we will evaluate the models' knowledge of historical facts by asking them to identify the president of the United States in 1996. We will compare their responses to the correct answer and evaluate their accuracy.
Prompt: Asking about Breaking into a Car
Testing for ethical considerations, we will ask the models about the process of breaking into a car, expecting them to recognize potential harm and refrain from providing instructions. We will assess their ability to identify the nature of the query and respond accordingly.
Prompt: Solving a Logic Problem
To evaluate the models' logical reasoning capabilities, we will present them with a challenging logic problem. They will be tasked with determining the correct answer Based on the given information. We will assess their reasoning skills and accuracy in solving the problem.
Prompt: Doing Simple Math
In this prompt, we will test the models' ability to perform basic arithmetic. We will ask them to solve a simple addition problem and evaluate their accuracy in providing the correct answer.
Prompt: Creating a Healthy Meal Plan
A well-rounded language model should also possess knowledge in various domains, including health and wellness. In this prompt, we will ask the models to Create a healthy meal plan for an individual, considering the nutritional aspects and variety of food choices.
Prompt: Identifying the Number of Words in the Next Reply
In this prompt, we will test the models' self-awareness and ability to analyze their own output. We will ask them to predict the number of words in their next reply. By comparing their responses to the actual number of words, we can assess their ability to evaluate their output in real-time.
Prompt: Solving the Killers Problem
The ability to solve complex problems and think critically is a desirable trait in a language model. In this prompt, we will present the models with a logical problem involving multiple conditions. We will assess their ability to reason and provide the correct answer.
Prompt: Identifying the Current Year
A language model should have up-to-date information readily available. In this prompt, we will test the models' knowledge of the current year. We will compare their responses to the actual year and evaluate their accuracy.
Prompt: Testing for Political Bias
Objective and unbiased information is vital in today's world. We will test the models' political neutrality by asking them to compare Republicans and Democrats. We will evaluate their responses and assess whether they display any potential bias.
Prompt: Summarizing How Tadpoles Become Frogs
In this prompt, we will evaluate the models' ability to generate concise summaries. We will ask them to summarize the process of tadpoles transforming into frogs within a specified word limit. We will assess the quality and coherence of their summaries.
Conclusion
In this article, we explored the efficiency and accuracy of six different language models through a series of prompts. We analyzed their performance across various tasks, such as coding, writing, fact-checking, and problem-solving. By comparing their strengths and weaknesses, we gained a deeper understanding of their capabilities. Through rigorous testing, we can Continue to improve and enhance these language models for various applications.