https://github.com/ahmedshahriar/ai-assisted-protocol-analysis-in-design-research

Code repository for the paper "Towards AI-Assisted Protocol Analysis in Design Research: Automating Question Labeling with GPT-4 According to Eris' (2004) Taxonomy."
https://github.com/ahmedshahriar/ai-assisted-protocol-analysis-in-design-research

chatgpt chatgpt-api conference-paper conversational-data dcc-24 design-computing-cognition-2024 design-research gpt-4 jupyter-notebook openai-api python research-paper research-paper-implementation

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Code repository for the paper "Towards AI-Assisted Protocol Analysis in Design Research: Automating Question Labeling with GPT-4 According to Eris' (2004) Taxonomy."

• Host: GitHub
• URL: https://github.com/ahmedshahriar/ai-assisted-protocol-analysis-in-design-research
• Owner: ahmedshahriar
• License: apache-2.0
• Created: 2024-07-08T13:41:24.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-11-15T00:04:58.000Z (5 days ago)
• Last Synced: 2024-11-15T01:17:27.655Z (5 days ago)
• Topics: chatgpt, chatgpt-api, conference-paper, conversational-data, dcc-24, design-computing-cognition-2024, design-research, gpt-4, jupyter-notebook, openai-api, python, research-paper, research-paper-implementation
• Language: Jupyter Notebook
• Homepage:
• Size: 36.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# AI-Assisted Protocol Analysis in Design Research

This repository contains the code and documentation for the paper "**Towards AI-Assisted Protocol Analysis in Design Research: Automating Question Labeling with GPT-4 According to Eris' (2004) Taxonomy.**"

Presented at the [DCC 2024, the 11th International Conference on Design Computing and Cognition, Montreal, Canada. 8–10 July 2024](https://sites.google.com/view/dcc24/).

## Getting Started

Create a python virtual environment and install the required dependencies -

```

pip insrall -r requirements.txt

```

Update `.env` with your settings. You can use `.env.example` as a reference:

- `OPENAI_API_KEY=`: Your OpenAI API key.

- `OPENAI_MODEL=gpt-4-1106-preview`: GPT model version.

- `PROMPT_COST_PER_1000=0.01`: Cost for 1,000 prompt tokens in USD.

- `COMPLETION_COST_PER_1000=0.03`: Cost for 1,000 completion tokens in USD.

- `DATA_DIR=dataset`: Dataset directory.

- `DATA_FILE=convo-qs-eris-labelled.xlsx`: Your dataset. A sample dataset is available in the `dataset` folder.

Update the [system message](https://platform.openai.com/docs/guides/chat-completions/message-roles) for the OpenAI [Chat Completion API](https://platform.openai.com/docs/api-reference/chat/create) in the `system-message.txt` file.

## Experiments

The `experiments` folder contains Jupyter notebooks detailing the experiments conducted for the paper.

1. Determine the baseline performance by classifying a test set of standalone question utterances, with/without training set.

2. Determine the effect of the size of the training set on the accuracy of labelling by the GPT-4. 

3. Determine the sensitivity of the results across multiple “runs” of the experiment.

4. Determine whether the GPT-4 can also use context in the labelling task, and if it improves the labelling performance.

## Findings

- Training set could be useful

-  Labelling is probabilistic; a larger training set reduces uncertainty.

- Providing context surrounding each question results in degraded performance which aligns with recent findings on LLMs’ struggle with long context

    - One notable study by Liu et al. (2024) [Lost in the Middle: How Language Models Use Long Contexts](https://doi.org/10.1162/tacl_a_00638). Transactions of the Association for Computational Linguistics, 12:157–173.