Testing llama 3 with Python 100 times so you don't have to
TLDRIn this video, the creator tests the AI model 'Llama 3' by asking it the same question 100 times using Python and the 'oLlama' package. The question involves a scenario with a cake and a plate, aiming to determine if the AI can consistently identify the correct room the cake is in. The experiment explores different prompting techniques to get the AI to provide a binary (A or B) answer. Initially, the AI struggles with consistency, but after refining the prompt, it correctly identifies the 'dining room' as the location of the cake 98% of the time. The video underscores the importance of crafting the right prompt for AI models and the potential for variability in their responses. It concludes that with the correct prompt, large language models can be highly accurate, but there's still room for improvement in their ability to infer correct answers when the correct response is not known in advance.
Takeaways
- 🤖 The video explores testing the AI model 'llama 3' with a specific question 100 times using Python to observe variations in responses.
- 🐍 The use of the `olama` package in Python is demonstrated to interact with large language models locally through a web server and Python bindings.
- 📚 The importance of correct prompting is emphasized, as it significantly affects the AI's ability to provide the correct answer.
- 🔁 Through iteration, the AI's response to a question about the location of a cake was found to be mostly correct when asked multiple times.
- 💻 The video shows that local processing on a MacBook Air can handle the task, although it's not the fastest.
- 📈 The AI's accuracy increases when given clear instructions, highlighting the need for precise language when interacting with AI.
- 🔍 The experiment involved tweaking the question format to receive a one-letter answer (A or B), which led to mixed results initially but improved with specific instructions.
- 📊 A loop was used to automate the process and test the AI's response over 100 iterations, revealing a 98% accuracy rate for the correct answer.
- 🤔 The video raises concerns about relying on AI when the correct answer is not known, as it may lead to incorrect conclusions if the AI's response is not accurate.
- 📝 The process of saving responses and extracting answers programmatically was demonstrated, which could be useful for further analysis or validation.
- 🔗 The takeaway from the experiment is that with the right prompting and testing, large language models like 'llama 3' can provide highly accurate responses.
Q & A
What was the initial question posed to both Llama 3 and Microsoft 53 in the previous video?
-The initial question was about a scenario where a cake is on a table in the dining room, a plate is placed on top of the cake, and then the plate is taken into the kitchen. The question is to determine which room the cake is currently in.
What was the outcome of the initial question in the previous video?
-In the previous video, Microsoft 53 appeared to have answered the question correctly, while Llama 3 seemed to have answered it incorrectly.
How did the user intend to interact with Llama 3 using Python?
-The user planned to use the 'olama' package in Python to interact with Llama 3 locally on their machine, asking the same question multiple times to see how the answers might vary.
What is the significance of using a one-letter response (A or B) in the experiment?
-The one-letter response (A or B) was an attempt to simplify the answer and make it easier for the model to provide a clear choice between the 'dining room' and 'kitchen', which were the two possible answers to the question.
What was the final outcome of asking the question 100 times using a loop?
-After running the question through a loop 100 times, Llama 3 answered 'dining room' 98% of the time, which was considered the correct answer, and 'kitchen' 2% of the time.
Why did the user decide to test the model multiple times?
-The user wanted to test the model multiple times to observe the variation in answers and to understand the model's consistency and reliability in responding to the same question.
How did the user ensure that the model was providing a single letter answer?
-The user gave specific instructions to the model within the Python script, requesting a one-letter response (A or B) and using additional prompts to refine the model's answers.
What was the user's strategy for handling the model's answers?
-The user created a list called 'answers' to store the model's responses. They then used a conditional structure to tally the occurrences of 'A' for 'dining room' and 'B' for 'kitchen', as well as a 'no answer' category.
What was the user's conclusion about the model's performance?
-The user concluded that Llama 3 performed well, providing the correct answer 98% of the time when asked the same question repeatedly. They noted the importance of crafting the correct prompt for reliable answers.
What was the user's final advice regarding the use of large language models?
-The user advised caution, noting that while large language models can be very helpful, one should not rely on them entirely, especially when the correct answer is not already known.
How did the user feel about the results of the experiment?
-The user expressed surprise and satisfaction with the results, noting that after initial skepticism, the model proved to be accurate in 98% of the cases after multiple iterations.
Outlines
🤖 Automating Language Model Interactions with Python
The speaker discusses their previous video where they posed a question to two different versions of a language model, with one seemingly answering correctly and the other incorrectly. They express a desire to test the same question to one of the models, Llama 3, 100 times to see the variability in responses. They then explain how to use the 'olama' package in Python to interact with large language models, including setting up a local web server and using Python bindings. The process involves installing the package, importing it, defining a response variable, and constructing a message with the question to be asked. The example question used is a logic puzzle about a cake and a plate, aiming to get a binary (A or B) response to simplify the process.
🔁 Experimenting with Iterative Questioning
The speaker details their process of asking the same question to the Llama 3 model multiple times using a Python loop. They observe that the model's responses vary, sometimes correctly identifying the 'dining room' and other times incorrectly choosing 'kitchen'. The speaker experiments with different prompts to try and get more consistent answers, noting the importance of how the question is phrased or 'prompted'. They find that when explicitly asked for a one-letter answer, the model tends to default to one option over the other. After several iterations and adjustments to the prompt, the model eventually provides the correct answer more consistently.
📈 Analyzing Model Responses through Multiple Iterations
The speaker creates a list called 'answers' to store the responses from the model after asking the question multiple times. They use a loop to run the question 20 times initially and then 100 times to gather a significant sample of responses. The goal is to analyze the performance of the model across multiple iterations. After running the loop, they tally the number of times the model provided the correct answer ('dining room') versus the incorrect one ('kitchen'), and also account for instances where no clear answer was provided. The analysis shows that the model correctly identifies the answer 98% of the time, which the speaker finds surprisingly accurate.
🤔 Reflecting on the Reliability of AI-generated Answers
The speaker reflects on the implications of relying on AI models for generating answers, especially when the correct answer is not already known. They express concern about the potential for misleading results if the prompt is not carefully crafted. The speaker concludes that while their testing showed the model to be accurate 98% of the time in this specific case, it's crucial to approach AI-generated answers with caution. They encourage viewers to subscribe for more content on Python, problem-solving, and working with large language models, and sign off with a promise to cover more topics in the next video.
Mindmap
Keywords
💡Llama 3
💡Python
💡oLlama
💡Web Server
💡Language Model
💡Automate
💡Multiple Choice Response
💡Prompting
💡Data
💡Loop
💡Accuracy
Highlights
The video explores the consistency of responses from the Llama 3 language model when asked the same question multiple times.
The experiment is conducted using Python and the Olama package to interact with the language model locally.
The initial question posed involves a scenario with a cake, a plate, and a dining room, aiming to determine where the cake is located.
The video demonstrates how to install and use the Olama package for Python to communicate with language models.
The language model's responses are tested for consistency by asking the same question in a loop 100 times.
The video shows how to format the question and request a specific type of response, such as a one-letter answer.
The experiment reveals that the language model's answers can vary, even when given the same input.
The video discusses the importance of crafting the correct prompt to get the desired response from the language model.
The Llama 3 model correctly identifies the location of the cake as the dining room 98% of the time in this experiment.
The video highlights the potential for bias in testing when the correct answer is already known to the person crafting the test.
The experiment concludes that with the right prompt, large language models can provide accurate responses most of the time.
The process of testing and refining prompts is emphasized as crucial for reliable outcomes with language models.
The video demonstrates the use of a loop to automate the process of asking the same question multiple times.
The experiment shows that the language model can struggle with providing a concise one-letter answer without additional guidance.
The video suggests that further testing and exploration are needed to understand the nuances of interacting with language models.
The video concludes by encouraging viewers to subscribe for more content on Python, problem-solving, and working with large language models.