EP136 - Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

Download the paper - Read the paper on Hugging Face

Charlie: Welcome to episode 136 of Paper Brief! I’m Charlie, your host, alongside our expert, Clio, on the intersection of tech and machine learning. Today we’re diving into a paper called ‘Chain of Code: Reasoning with a Language Model-Augmented Code Emulator’. Clio, could you give us a quick rundown of what makes Chain of Code interesting?

Clio: Absolutely, Charlie. Chain of Code, or CoC for short, is this nifty extension to improve code-driven reasoning in language models. You see, LMs can solve complex problems by breaking them down into steps, a method known as Chain of Thought. CoC takes it further by enabling LMs not only to write code but also to simulate parts that can’t be executed. It’s like it’s mimicking an interpreter, writing pseudocode along with executable code.

Charlie: That sounds pretty clever, using language models to ’think in code’. But how exactly does it handle tasks that are tough to code, like detecting sarcasm?

Clio: Great question. Traditional code execution might choke on such nuanced tasks, but CoC introduces something called an ‘LMulator’, which is part-programming, part-language prediction. It allows the LM to handle ambiguous or subjective tasks by predicting outcomes, which then integrate with the rest of the code. It’s the best of both worlds – precise computation and nuanced reasoning.

Charlie: So it goes beyond just mathematical operations to more complex reasoning. Can you give us an example of how this LMulator works in practice?

Clio: Sure! Imagine you want to know how often a writer is being sarcastic in a paragraph. CoC can write code using a helper function like ‘is_sarcastic(sentence)’ where the LM makes a guess and generates a boolean that’s used by the rest of the program.

Charlie: I see. It’s sort of ‘cheating’ the system to our advantage. How does this CoC compare with plain old Chain of Thought or the direct answer approach?

Clio: It’s not so much ‘cheating’ as it is augmenting our toolkit. The research shows CoC actually outperforms Chain of Thought and the direct answer method, particularly on complex tasks like the BIG-Bench Hard, and even against human raters on some algorithmic problems.

Charlie: That’s impressive. It’s not every day you hear about a system outsmarting humans on algorithmic tasks. What are the implications for future applications?

Clio: Well, it certainly broadens the scope of problems LMs can tackle, from simple calculations to answering real-world questions with a mixture of computation and semantics – things like AI in robotics or complex data analysis could greatly benefit.

Charlie: And speaking of benefits, do you think this approach will only work with super large models, or is it scalable to smaller ones too?

Clio: Actually, scalability is one of the key strengths of Chain of Code. Unlike other techniques that only emerge with huge models, CoC is designed to work well with both large and smaller LMs, which is fantastic for its potential widespread use.

Charlie: Very exciting stuff. Thank you for breaking down ‘Chain of Code’ for us, Clio. Any final thoughts?

Clio: Just that it’s incredible to see how we’re continually pushing the boundaries of what machine learning can do. I’m eager to see where CoC takes us next!

Charlie: Absolutely, Clio. And to our listeners, thanks for tuning in to episode 136 of Paper Brief. We’ll catch you next time as we explore another cutting-edge piece of research. Stay curious!