Hot off the Wires 🔥

We're eavesdropping on the smartest minds in research. 🤫 Don't miss out on what they're cooking up! In this section, we dissect some of the juiciest tech research that holds the key to what's next in tech.⚡

Remember this iconic tweet by world-renowned computer scientist Andrej Karpathy, when the earliest version of GitHub Copilot made the headlines?

This was a testament to the productivity gains software devs can achieve through AI tools like GitHub Copilot (check out our previous edition, where we dived deep into this).

While AI-powered code generation is revolutionizing how we write software, it's not without its pitfalls. Sure, it can churn out large blocks of code faster than you can say "Hello, World!", but it also serves up a hefty side of bugs that are trickier to find than a needle in a haystack.

Enter OpenAI with their latest brainchild: CriticGPT - an AI model designed to detect errors in code written by other AI models like their very own ChatGPT!

When we first saw the title of OpenAI's announcement blog, "Finding GPT-4's mistakes with GPT-4", we couldn't help but be reminded of this iconic dialogue from the Bollywood movie Dabangg 3:

It seems OpenAI has taken this philosophy to heart in the world of AI!😂

Forging the Fundamentals

Before we dive deeper into how CriticGPT is set to become the Sherlock Holmes of the coding world, let's break down some jargon:

So, what’s new?

Now, you might be thinking, "Wait a minute, we're using AI to catch mistakes made by... AI?" And you'd be right! It might take a while to wrap your head around this idea.

Recently, there have been efforts where LLMs were asked to evaluate their responses through "self-correction," which worked well only when given extra information. It's like asking a student to grade their own test - without the answer key, it's not very effective.

Other techniques, known as "scalable oversight," involve debates with humans or recursive reward modelling. These methods aim to enhance human judgment rather than improve the base model itself. Think of it as giving the teacher better tools to grade papers, instead of making the students smarter. However, these methods were primarily used in simpler scenarios like MCQ answering.

CriticGPT takes this idea and demonstrates how scalable oversight can help humans in a more realistic and complex setting: code review.

Code reviews are crucial for maintaining high-quality software, but reviewing AI-generated code presents unique challenges. How do you ensure thoroughness without getting bogged down in nitpicks? How do you catch subtle bugs that might slip past even human reviewers?

This is exactly where CriticGPT shines bright - researchers used a method similar to the RLHF used for ChatGPT but with some twists!

Under the hood…

CriticGPT is built on the foundation of GPT-4 and trained using a variant of RLHF. Here, we’ll look at how it works its magic 🎩🪄

Training Data: The training data comprises pairs of questions (such as some coding tasks) & answers (the LLM’s response to the task, containing generated code). This data includes two types of examples:

• Human-Inserted Bugs: Human contractors “tamper” the answers - they snuck subtle bugs into model-written code, then documented them just like a code review catch. These "adversarial" bugs were designed to be tricky for basic LLM critics, ensuring CriticGPT would be well-equipped to handle tough situations.

• Human-Detected Bugs: Real-world bugs that were identified by humans from the training pipeline of ChatGPT were also included, providing CriticGPT with a taste of the unexpected errors it might encounter.

CriticGPT Training Process:

1. Taking the question & answer as input, CriticGPT (before training) is asked to output structured critiques, highlighting portions of the code with issues, and commenting on them

2. Multiple critiques were sampled from this initial version of CriticGPT

3. Human evaluators compared and rated these critiques, giving scores based on various quality features, such as comprehensiveness, accuracy, and freedom from nitpicks, along with the ability to catch specific bugs (hello, human feedback!)

   

   The framework used for comparison of LLM-generated critiques (source: CriticGPT paper)

4. These human ratings were used to train a reward model that assigns a score to each critique, reflecting its perceived quality

5. CriticGPT was then fine-tuned with the help of this reward model, optimizing its ability to generate awesome critiques. This was achieved using the Proximal Policy Optimization (PPO) algorithm and a clever new technique called Force Sampling Beam Search (FSBS).

What’s the Intrigue?

The probability of catching a bug increases with the number of claims that a critique makes. However, when an LLM is asked to point out a comprehensive & long list of bugs, it may often start hallucinating & nit-picking on unnecessary issues.

To tackle this challenge, a novel technique called Force Sampling Beam Search (FSBS) was introduced while fine-tuning CriticGPT.

FSBS makes CriticGPT generate critiques that highlight multiple erroneous snippets from a model-generated code answer using controlled sampling. These critiques are scored based on the original reward model and the number of highlights. The combined score is maximized to balance the tradeoff between being comprehensive (more highlights) and avoiding hallucinations & nitpicks (higher reward scores).

Why does this matter?

CriticGPT’s critiques often outperformed human critiques, catching more inserted bugs.

Moreover, CriticGPT also helps humans write more comprehensive critiques; those assisted by CriticGPT outperformed unassisted reviewers 60% of the time.

Now, let's talk about real-world applications. Companies in software development, cybersecurity, and financial technology can leverage this technology to ensure their code is robust and error-free. Imagine…

• A fintech startup 💲 using CriticGPT to catch potential security vulnerabilities in their AI-generated code before deployment

• A game development studio 🕹️ leveraging it to identify bugs in procedurally generated game levels

• An e-commerce giant 🛒 integrating CriticGPT into their CI/CD pipelines, catching issues early and reducing costly downtime

The possibilities are endless!

While CriticGPT isn't perfect (it can still make mistakes), it's a significant step towards better evaluation of advanced AI systems. So, the next time you're knee-deep in a code review, remember – you might soon have an AI assistant helping you catch those elusive bugs.🔍

Key Takeaways

(Screenshot this!)

10x Your Workflow with AI 📈

Work smarter, not harder! In this section, you’ll find prompt templates 📜 & bleeding-edge AI tools ⚙️ to free up your time.

Fresh Prompt Alert!🚨

Ever dreamed of having a YouTube podcast that everyone's buzzing about? 🌟 Well, today's your lucky day! This week’s Fresh Prompt Alert is your ticket to becoming a podcasting sensation.

Picture this: you're diving deep into hot industry trends like AI, blockchain, and telehealth, chatting with experts who know their stuff. Ready to turn those ideas into a script that'll have your followers hooked?

Let's make it happen! 👇

3 AI Tools You JUST Can't Miss 🤩

• 👩‍💻 Upsend - AI-powered mock coding interviews with personalized feedback

• 🛣️ GuideJar - Create interactive, easy-to-follow, AI-powered guides and demos

• 📢 FounderPal - AI marketing platform for Solopreneurs

Spark 'n' Trouble Shenanigans 😜

How long do you think it'll be before this becomes reality?? 😋