AI @ Hack4SocialGood

Draft / Working Paper

Introduction

In recent years, the use of Large Language Models (LLMs) has become increasingly popular at hackathons. These AI tools offer developers the ability to rapidly prototype and build intelligent applications, providing a competitive edge in fast-paced, time-sensitive environments. In this introduction, we will discuss the benefits of using LLMs at hackathons, provide an overview of alternative technical solutions, and address the data protection risks associated with using such tools.

Goals

The primary benefit of using AI at hackathons is the acceleration of the development process. By leveraging pre-trained models and APIs, developers can quickly incorporate advanced functionality into their applications without having to spend significant time and resources on training their own models. This enables teams to focus on solving the core problem at hand and delivering a more polished product within the limited timeframe of a hackathon.

Additionally, LLMs can help level the playing field for participants with varying levels of expertise. Teams with limited experience in natural language processing can still leverage the power of AI by incorporating pre-built models into their projects, while more experienced developers can build upon these models to create more advanced solutions. Conversational interfaces make it particularly quick and easy to get started.

Technical alternatives

While LLMs like OpenAI's GPT-4 offer a quick and easy way to incorporate natural language processing into hackathon projects, there are alternative technical solutions available. One such alternative is to train custom models using frameworks like TensorFlow or PyTorch. This approach requires more time and resources but allows for greater control over the model's architecture and training process, potentially resulting in better performance on specific tasks.

Another alternative is to use open-source pre-trained models, which can be fine-tuned for specific tasks using transfer learning. This approach combines the benefits of pre-built models with the flexibility of custom training, allowing developers to tailor the model to their specific needs without starting from scratch. You can find many of these in a public model registry such as https://huggingface.co/models or https://replicate.com/explore

Data protection risks

As with any technology that involves the processing of data, there are potential data protection risks associated with using LLMs at hackathons. Participants should be aware of the privacy implications of using third-party LLM services and ensure that they have obtained the necessary consent and level of due dilligence.

It is crucial to consider the security of the data being processed by LLMs. Hackathon organizers should establish clear guidelines for data handling and storage, and participants should take appropriate measures to protect sensitive information. This may include encrypting data at rest and in transit, implementing access controls, and regularly monitoring for potential security breaches.

Presenting your work

When presenting and making public your work based on AI models, it is essential to consider the following best practices to ensure that your work is accessible, understandable, and engaging to a broad audience:

1. Clearly explain the problem you are addressing: Begin by providing context and describing the problem or challenge that your work aims to address. This will help your audience understand the significance of your research and how it fits into the broader field of AI and natural language processing.

2. Introduce the model: Provide an overview of the model (e.g. LLM) as well as the techniques you used to train or refine it (e.g. RAG - Retrieval Augmented Generation), you used in your project, including its architecture, training process, and any pre-processing or post-processing steps you took. Be sure to highlight any unique aspects of your model or approach that set it apart from other similar models.

3. Describe your dataset(s): Discuss each dataset you used for training, fine-tuning, or evaluating your model. Provide details about its size, composition, and any relevant pre-processing steps. If you used a publicly available dataset, be sure to include a citation and a link for others to access it.

4. Highlight your results and findings: Share the results of your experiments, including any quantitative metrics (e.g., accuracy, perplexity, or BLEU score) and qualitative examples (e.g., generated text or summaries). Be sure to discuss the strengths and limitations of your model and any insights you gained from your research.

5. Discuss potential applications and future work: Consider the potential applications of your work and how it might be used to solve real-world problems. Additionally, outline any planned or potential future work, such as further experimentation with different model architectures or datasets, or the development of more advanced systems.

6. Make your code and models available: To promote transparency and reproducibility, make your code and trained models publicly available on platforms like GitHub or Hugging Face. This will allow others to build upon your work and contribute to the broader AI community.

7. Share your work through multiple channels: To maximize the visibility and impact of your research, share your work through various channels, such as academic conferences, workshops, and journals, as well as social media platforms, blogs, and online forums.

By following these best practices, you can effectively present your work at Hack4SocialGood, contributing to the ongoing advancement of AI and natural language processing technologies in the social work sector.

Please contact our team if you have any questions!