Advanced Reasoning with Large Language Models with Chain of Thought Prompting | Paper explained!

hey everyone welcome to the global NLP lab a place where we discuss the latest in natural language processing subscribe to stay up to date with our latest content large language models with 100 billion parameters or more are able to achieve strong results on a wide range of tasks even with little or no training examples however even the largest language models can still struggle with certain multi-step reasoning tasks such as math word problems and Common Sense reasoning well researchers at Google came up with a method called Chain of Thought prompting that aims to improve the reasoning abilities of language models this method enables the model to decompose multi-step problems into intermediate steps allowing the model to solve complex reasoning problems that are not solvable with standard prompting methods in today's video we are going to take a look at Chain of Thought prompting discussing the benefits of the approach as well as the experimental results so let's get into it now you may be wondering how does Chain of Thought prompting differ from standard prompting with standard prompting displayed on the left part of this figure the model is given examples of input output pairs formatted as questions and answers before being asked to predict the answer for a test time example this method has been popularized by the GPT 3 model and has shown to be effective in a range of NLP tasks with Chain of Thought prompting shown on the right the model is prompted to produce intermediate reasoning steps before giving the final answer to a multi-step problem this is achieved by expanding the examples in the prompt to contain the detailed reasoning process such as Roger started with five balls two cans of three tennis balls each six tennis balls the answer is 11. in the example when presented with an unseen question the LM is required to produce the reasoning steps before outputting the final answer the idea here is that a model generated Chain of Thought would mimic an intuitive thought process when working through a multi-step reasoning problem Chain of Thought is a simple and intuitive technique that can be used with any off-the-shelf language model some of the benefits of this prompting approach are that it forces the model to decompose the problem into several reasoning steps allocating more computation to them before producing The Final Answer furthermore Chain of Thought is easily interpretable by humans and is applicable to a wide range of problems such as common sense reasoning without the need for a large training data set or modifying the model's weights so how does Chain of Thought prompting perform in practice the researchers tested it on a variety of reasoning tasks including arithmetic geometry logic and even some forms of Common Sense reasoning one area where language models typically struggle is with arithmetic reasoning or solving math word problems the researchers tested their method on two benchmarks in this area multi-aerith and GSM 8K they evaluated both the lat NDA collection of language models ranging from 422 M to 137b parameters as well as the Paw LM collection of language models ranging from 8B to 540b parameters they found that using standard prompting led to relatively flat scaling curves C figure in the top right meaning that increasing the scale of the model did not substantially improve performance however when using Chain of Thought prompting they found that increasing model scale led to improved performance that substantially outperformed the Baseline anchor but it's not just arithmetic reasoning where Chain of Thought prompting shows promise the researchers also found that it improved performance on a variety of other reasoning tasks including geometry logic and even some forms of Common Sense reasoning this is a significant development as it allows language models to approach problem solving in a more human-like manner breaking down complex problems into intermediate steps that are solved individually now you might be wondering will switching to Chain of Thought lead to better performance for my task of Interest there are several aspects to consider the first aspect is the complexity of your target task if your task involves complex multi-hop reasoning Chain of Thought prompting might definitely help otherwise for simple tasks it might make little to no difference second are you planning to use a large language model to solve your task as discussed previously Chain of Thought seems to have the biggest effect when using language models larger than 100 billion parameters if you are not planning to use such a model switching to Chain of Thought won't probably make a difference finally if you're not able to get further gains on the task even using the largest language models this might be an indicator that you need to try an alternative prompting method such as Chain of Thought so there you have it an overview of the Chain of Thought prompting it's a really simple and effective method that improves the reasoning abilities of language models we're looking forward to seeing how prompting methods evolve in the future we hope you found this video informative please drop a comment if you have any questions or thoughts to share that's all for now thanks for watching and we'll see you next time

Channel: The NLP Lab

Views: 10,416

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Id: sYKU9zC5RKs

Length: 6min 11sec (371 seconds)

Published: Mon Jan 16 2023