Assessing the Performance of Code LLMs

In the world of computer programming, we've seen some amazing changes. Big language models, which we can think of as super-smart code helpers, have come into play. These helpers can write and understand code in many languages. They can follow complex instructions, making life easier for programmers. But, just like that friend who can never find their keys, these smart helpers have their weaknesses when things get tricky.

#The New Kid on the Block

So, these smart code helpers—let’s call them Code LLMs—are great at their jobs, but they face a challenge: how well can they handle unexpected twists in the input? That's like asking a chef how well they can cook when the ingredients keep changing! This is where DegradePrompter comes in. Think of it as a tool that pokes and prods these code helpers to see how they react when the going gets tough.

#Testing the Waters

We decided to put several types of Code LLMs to the test. We included both Open-source Models—like free apps you grab from the internet—and some commercial ones, which are like fancy restaurant meals you pay a lot for. The goal? To see how well these models perform when faced with tricky questions, prompts, and all sorts of curveballs.

#What Happens When Things Go Wrong?

When we pushed these models with different challenges, we saw quite a range of reactions. The open-source models, in many cases, wobbled like a toddler trying to walk. For some, their ability to create functioning code dropped by 12% to 34%. That’s quite a dip! On the other hand, the Commercial Models held their ground better, losing only 3% to 24% of their coding prowess, proving that in the coding world, you often get what you pay for.

#The Balancing Act

One of the big questions we asked was whether size matters. Do bigger models mean better performance? Generally, yes! Larger models often did better, but not always. It's a bit like how some tall people can’t play basketball very well.

#Learning from Mistakes

To help these models perform better, we figured we’d give them a boost with our Guided Prompting technique. Think of this as giving someone directions while they're trying to find their way in a new city. By helping them focus on what matters most, we hoped to improve their performance, even when things got confusing.

#A Cautionary Tale

We had our fun playing with these code helpers, challenging them, and seeing how they respond. But the adventure also showed us that many open-source models still have a lot of room for improvement. They are like teenagers just starting to learn how to drive—they need practice and guidance!

#What’s in a Name?

We also discovered that not all code helpers are made equal. Some families of models did better than others. For example, one family of Code LLMs showed quite a bit of strength against the trick questions, while others seemed to get tripped up easily, like someone trying to run in flip-flops.

#A Mixed Bag of Results

While our guided prompting helped some models bounce back, it was not a guaranteed fix. For a few, it felt more like a Band-Aid than a cure. This suggests that some models may need a bit of a makeover to truly boost their performance.

#Future Explorations

Going forward, we have plenty to think about! It would be interesting to see how these models fare with different programming languages. Can they handle the challenge of Java or C++ as well as they do with Python? That’s a question that deserves an answer!

We could also explore what happens when we play around with how instructions are given. Do they handle subtle shifts in language? That could be fun—and enlightening!

#The Need for Better Helpers

The main takeaway is clear: programming helpers have come a long way, but we still have work to do. Just like a good chef who keeps experimenting to find the perfect recipe, we need to keep tweaking and testing these models to ensure they can handle whatever we throw at them. Who knows how amazing they could become in the future?

#Wrapping Up

In conclusion, our studies show that while smart code helpers are fantastic, they need a little more training to handle unexpected situations. With ongoing efforts and clever ideas, we’re sure to see improvements. If coding is a journey, then the road ahead is wide open for adventure!

And as programmers, we can enjoy the ride—just remember to buckle up because it might get bumpy!