Improving Language Models with New Decoding Techniques

Large Language Models (LLMs) are like fancy robots that can write text, but they can struggle when it comes to generating very structured Outputs like computer code or math formulas. It’s a bit like asking a chef who specializes in desserts to whip up a soufflé-just because they’re great at one thing doesn’t mean they can tackle everything.

To help with these challenges, some clever folks came up with constrained decoding approaches. This means they carefully guide the LLMs to make sure they follow certain rules when creating their outputs. Think of it as a set of instructions for a game that the LLM needs to follow to play properly.

In this piece, we will talk about a specific type of constrained decoding called Grammar-constrained Decoding (GCD). This is where the outputs of the LLMs must follow certain grammar rules. However, there’s a catch! We found out that these methods can sometimes mess up the LLM's natural way of generating text and can lead to poor-quality outputs.

That’s where grammar-aligned decoding (GAD) comes into play! We’ll also introduce a new approach called Adaptive Sampling with Approximate Expected Futures (ASAp). The goal of ASAp is to help the LLMs create outputs that follow the rules while still sounding good and making sense.

In simple terms, we want to make sure our robot chef can still make delicious dishes while following the instructions without breaking a sweat.

#The Problem with GCD

GCD is like telling the LLM, “Hey, you have to write this specific kind of document, so here are the rules.” While it does help the LLM stay on track, it can also distort the probability of various outputs. Imagine this: You ask the LLM to write a story about a cat, but the GCD method ends up making it write about a cat who suddenly starts dancing like a robot. That’s funny, but not what we wanted!

We realized that GCD can cause a problem. The outputs might be grammatically correct, but they can be so unlikely according to the LLM that it feels like a bad joke. So, we needed a better way to align the text generated by LLMs with the grammar rules.

#Meet GAD

So, what is GAD? It’s a new way of making sure that when LLMs generate text, it not only follows grammar rules but also fits within the probabilities of what the LLM naturally wants to write. It’s like giving our robot chef a set of delicious recipes that taste great and meet specific dietary needs.

GAD helps ensure that the LLM produces outputs that are both sensible and adhere to the grammar rules. For instance, if we say, "Write a love letter," GAD guides the LLM to generate a letter while maintaining its natural flair and personality.

#ASAp to the Rescue!

Now, let’s talk about ASAp, our shiny new tool. Imagine it as giving our robot chef a new set of cooking gadgets that help him create better dishes over time.

ASAp works by sampling outputs repeatedly while keeping track of which outputs work and which don’t. It’s much like how an aspiring chef learns by trying different recipes and adjusting them based on feedback.

Rather than just forcing the LLM to follow grammar rules and risking poor quality, ASAp allows it to explore while gradually learning which paths lead to tasty food-which, in our case, means good text!

#A Walkthrough of How ASAp Works

First, ASAp starts off using the standard GCD approach, figuring out what outputs are valid based on the grammar rules. However, instead of sticking strictly to one method, ASAp keeps track of the outputs it has seen so far.

With each new output generated, ASAp recalibrates how it thinks the LLM can stay within the grammar rules. It’s like a GPS system that learns the best routes based on past traffic patterns to avoid jams in the future.

The algorithm keeps iterating, sampling outputs one after another and learning from what worked and what didn’t. Over time, it becomes better at producing the right outputs without losing the fun and creativity that the LLM can bring to the table.

#Evaluation and Results

When we tested our ASAp approach, it often outperformed the standard methods, meaning it generated outputs that were not only grammatically correct but also aligned better with what the LLM would naturally generate.

In our experiments, we showed that ASAp can take the lead, especially in tasks like code generation and structured language processing. It’s like how a student improves in math when given more practice and guidance; ASAp gets better the more it samples outputs.

#The Good, the Bad, and the Future

While ASAp has shown excellent results, we must admit that it’s not perfect. There are still instances where it takes time to converge on the desired output. It’s like training for a marathon; it doesn’t happen overnight.

As we move forward, there’s plenty of room for improvement. The future holds promising ideas like mixing ASAp with smarter search methods to help the LLM explore more efficiently. Think of it as upgrading our robot chef’s tools to create even more exquisite dishes more quickly.

#Conclusion

In conclusion, LLMs are phenomenal tools, but they can get tangled when tasked with structured outputs. With GAD and ASAp, we’ve found a way to help them create beautiful and grammatically precise content without losing their flair.

While we still have some challenges ahead, the work we’ve done lays a strong foundation for future developments. Just like a chef perfects their craft, LLMs can learn and adapt over time to deliver outputs that meet both structured requirements and the nuances of human language.

So, next time you ask a language model to write something structured, you can do so knowing that tools like ASAp are there to help it shine! That’s something to cheer for-like a successful soufflé rising in the oven!