Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

 

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Here is a paper titled Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Let’s talk about it.

This is interesting. It's a prompting method for eliciting multi-step reasoning from language models. The idea is to show the model a few examples of a task with intermediate reasoning steps, and this can improve performance quite a bit compared to just showing the model input-output pairs!

This is unlike how we usually prompt language models, right?

Right, in a standard prompting scenario you just provide input-output examples. Like for a translation task, you'd give the model some examples of sentences in one language and their translations in another. The insight here is that for tasks that require reasoning, we can give the models examples of how to think through the problem.

I can see how that would help the model break down a complex problem into smaller, more manageable steps. Can you elaborate on the types of tasks that improved with chain-of-thought prompting?

They tested on several benchmarks like math word problems, commonsense reasoning and symbolic reasoning tasks and saw a pretty good boost in performance. Math word problems especially benefitted - the models were actually able to generate a chain-of-thought that resembles a step-by-step solution!

That's a good result considering math is typically a challenging area for language models. This seems like it could also improve the interpretability of language models, no?

Yes, absolutely! By seeing the chain of thought, we can better understand how the model arrived at its answer. This could help us debug errors, identify biases, and generally make models more transparent.

So does this method work on all models? Even the smaller ones?

Well, the paper suggests that chain of thought reasoning is an emergent property of larger models. This means it only really works well for big models, around 100B parameters or more. Smaller models can generate the intermediate steps, but they often don't make much sense.

Why do you think that is?

My intuition is that smaller language models just don’t have a good enough understanding of the world to reason effectively. That's also why scaling up the model size is so important for improving performance, as the authors note. And it makes sense--reasoning is a complex task and it likely requires a lot of knowledge and understanding of how things work.

What about tasks that require a lot of world knowledge - did the chain-of-thought prompting help there as well?

Yes, they tested on commonsense reasoning tasks like the StrategyQA benchmark where you need to infer a multi-hop strategy to answer questions, and it did improve things.

So for these tasks, is the chain of thought just a restatement of the knowledge the models already have? Or does generating the chain of thought actually help them reason and come to a conclusion?

The paper explores this question by presenting an alternative configuration where the chain of thought prompt is only given after the answer. The results suggest that the sequential reasoning embodied in the chain of thought is useful beyond just activating knowledge.

The paper also mentioned symbolic reasoning - how did the models do there?

The paper shows chain-of-thought prompting can lead to improved performance on symbolic reasoning tasks, including facilitating length generalization to inference-time inputs longer than those seen in the few-shot examples

And this closes our discussion of Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Thank you!