ChatGPT and other LLMs

Large Language Models (LLMs) can be a powerful tools for assisting with statistics research, especially when it comes to the computational aspects. They can provide insights into statistical methods, guide you through code implementations, and help troubleshoot common problems in statistical analysis. This guide provides tips and tricks to make the most of LLM capabilities in the field of statistics.

Tips and Tricks for Using LLMs for Statistical Research

A central part of using LLMs effectively is creating effective prompts. In general, prompts should be specific and include enough relevant keywords to guide the LLM to the correct answer. The following section shows examples of how to create prompts for different scenarios in statistical research. These prompts were tested using ChatGPT, but the principles should apply to any commercial-scale LLM

Specifying Your Problem

To get the best results, clearly describe your statistical problem. Mention specific aspects, such as:

• Statistical methods: Specify the methods (e.g., linear regression, Bayesian analysis, hypothesis testing) you need help with.
• Data characteristics: Describe the data structure, including the number of variables, sample size, and any data challenges (e.g., missing data, non-normal distributions).
• Software or programming language: Indicate the programming language or software you are using (e.g., R, Python, SPSS) for tailored code suggestions.

Example Prompt:

I need help implementing logistic regression in Python using scikit-learn on a dataset with imbalanced classes. Can you provide guidance?

Generating and Explaining Code

Many LLMs (e.g. ChatGPT and Perplexity) can generate and explain statistical code for popular languages like Python and R. To get the most helpful responses:

• Request code snippets for specific tasks (e.g., data cleaning, model fitting, evaluation).
• Ask for explanations to better understand each step, especially if you’re learning the language or technique.
• Iterate on code: If the initial code needs adjustments, describe what isn’t working or ask for specific modifications.

Example Prompt:

Can you provide a Python code example for implementing k-means clustering and explain each step?

Troubleshooting and Debugging

LLMs can also assist with troubleshooting code errors or debugging. When asking for help:

• Share the error message and as much context as possible. Include code snippets in the prompt when possible.
• Describe the intended outcome so ChatGPT can better identify possible issues.

Example Prompt:

I'm getting a TypeError in my Python code while trying to calculate the mean of a list. Here is my code and error message. Could you help me debug it?

Choosing the Right Statistical Method

When uncertain about the best method to use, LLMs can offer suggestions based on your research needs:

• Provide research goals and data characteristics to get method recommendations.
• Specify assumptions or preferences if you have any (e.g., parametric vs. non-parametric methods).

Example Prompt:

I'm analyzing survey data with ordinal responses and would like to find relationships between variables. What statistical methods would you recommend?

Handling Data Processing Tasks

LLMs can guide you through data preprocessing, such as:

• Data cleaning (handling missing values, outliers).
• Data transformation (normalization, standardization).
• Feature engineering (creating new features or selecting the most relevant ones).

Example Prompt:

How can I handle missing data in Python for a machine learning model? Could you show different imputation methods?

Interpreting Statistical Results

For interpreting results, LLMs can explain outputs from statistical software or clarify statistical metrics. This can be helpful for:

• Understanding output from statistical tests (e.g., p-values, confidence intervals).
• Interpreting model performance metrics (e.g., accuracy, AUC, RMSE).
• Analyzing coefficients and effect sizes in regression models.

Example Prompt:

Can you help me interpret the output of a logistic regression model in R, especially the meaning of the coefficients and the p-values?

Limitations and Considerations

While LLMs are versatile tools, keep in mind:

• Verify critical results: Use LMM suggestions as guidance, but verify results and conclusions independently.
• Limited handling of complex code: LMMs may struggle with intricate, multi-file projects or extensive datasets.
• No real-time data: LLMs typically do not access real-time databases or internet resources for data; it relies on the information provided within the prompt.

Accessing Available LLM Resources

ChatGPT

ChatGPT is a large language model (LLM) built on the transformer architecture, trained using unsupervised learning on vast amounts of text data and fine-tuned with reinforcement learning from human feedback (RLHF) to generate coherent, contextually relevant responses, making it a versatile tool for statistical reasoning, hypothesis generation, and exploratory data analysis. If you are new to ChatGPT, consider checking out this Introduction to ChatGPT to understand its basic functionalities, use cases, and how to interact with it effectively.

ChatGPT can be used through the website interface or through dedicated desktop and mobile apps. It is highly recommended that you create an account on the ChatGPT website. Users without an account will be forced to use a smaller version of the ChatGPT model.

GitHub Copilot

GitHub Copilot is an AI-powered code completion tool, leveraging OpenAI's advanced language models to generate context-aware programming suggestions, automate repetitive tasks, and assist with complex algorithm implementation, including statistical and data-driven analyses. See GitHub Copilot for more information on how to access this resource.

Gemini

Google Gemini is LLM designed specifically for multimodal functionality, including text, image, video, and audio formats. Potentially useful for tasks such as "are there any typos in this figure?". Google Gemini can be accessed through the website interface or dedicated mobile apps.

Perplexity AI

Perplexity AI is a sophisticated search engine that leverages large language models (LLMs) like GPT-4 and Claude 3 to perform real-time web crawling, natural language processing, and information synthesis, delivering concise, citation-backed responses to user queries while employing advanced techniques such as contextual memory and multi-model integration to enhance search relevance and accuracy.

Perplexity can be accessed through its web interface or through dedicated mobile apps. CMU Students are able to receive one year of Perplexity Pro for free through the following process:

1. Visit the Perplexity AI website and create an account using your CMU email address
2. Visit https://www.perplexity.ai/finance/CMU. A popup should show that indicates that CMU students are able to receive a Pro subscription for one year.
3. Click the popup and follow the prompts to receive your pro subscription

Other LLM Resources:

See https://guides.library.cmu.edu/QatarLibrary/AI4research for other LLMs tailored for specific research tasks.