SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures

Summary

Large language models have shown promise in reasoning tasks, but various prompting methods make assumptions about universal reasoning processes. The self-discover approach instead aims to uncover the unique reasoning structure for each task. It works in two stages - first, selecting, adapting and implementing reasoning modules into a structured plan based on the task. Second, using this plan to solve problems. Experiments show self-discover enhances reasoning capabilities efficiently across tasks requiring world knowledge. Analyses also demonstrate the importance of each step in the process, and the flexibility of applying self-discovered structures across models.

Takeaways

• 😀 LLMs have made progress in generating text and solving problems, but more reasoning capabilities are needed
• 🧠 Researchers use prompting methods inspired by human reasoning to enhance LLM reasoning
• 🚀 Self-discover aims to uncover the unique reasoning structure for each task
• 🔍 Self-discover has 3 key steps: select, adapt and implement reasoning modules
• 📈 Self-discover outperforms other methods on challenging reasoning tasks
• 💡 Self-discover is efficient, requiring fewer inference calls
• 🌟 Self-discover is effective for tasks requiring world knowledge
• ⚙️ All 3 self-discover steps contribute to enhanced LLM performance
• 🔀 Self-discover blends prompting methods into a versatile approach
• 🤖 Self-discover allows flexibility in how models approach reasoning tasks

Q & A

What are large language models (LLMs) and what have they recently shown capabilities for?

Large language models are neural networks trained on massive amounts of text data. Recently they have shown the ability to generate coherent texts and follow instructions thanks to the power of Transformers.

What is the goal mentioned in developing new methods for enhancing LLMs' reasoning capabilities?

The goal is to push LLMs even further in their ability to reason and solve complex problems more effectively.

What are some examples of prompting methods inspired by human reasoning that have been tried with LLMs?

Methods inspired by how humans tackle problems step-by-step, break big problems into smaller pieces, or step back to understand the full nature of a task better.

What limitation is shared by these human reasoning-inspired prompting methods?

They assume one universal reasoning process will work for all tasks, which isn't always the case. Each task has its own unique puzzle to solve.

How does the self-discover approach aim to address this limitation?

Self-discover tries to uncover the unique reasoning structure within each task, like how humans would come up with a game plan for a new problem using basic strategies.

What are the two main stages of the self-discover process?

First it figures out the task's unique reasoning structure by guiding the LLM through steps to generate a plan. Then the LLM uses this plan to solve actual problems.

What are some of the advantages offered by the self-discover approach over other methods?

It combines different reasoning strategies, is computationally efficient, and provides insights into the task in an understandable way.

On what types of challenging reasoning tasks did self-discover perform particularly well?

It excelled on tasks requiring world knowledge and did pretty well on algorithmic challenges too.

Where did the self-discover approach stumble in the analysis?

Mainly on computation errors in math problems.

What do the results suggest about the potential future applicability of self-discover?

Its ability to transfer reasoning structures between models shows promise for using structured reasoning to tackle challenging problems with LLMs going forward.

Outlines

00:00

😊 Introduction to Larges Language Models and Reasoning Methods

The introduction provides background on large language models (LLMs) and their ability to generate text and follow instructions. It discusses different prompting methods researchers have tried to enhance LLMs' reasoning capabilities by mimicking human problem-solving approaches. However, each method assumes a one-size-fits-all reasoning process rather than understanding each task's unique structure, which is key to efficiently solving it.

05:00

😀 Stage 1 of Self-Discover: Task-Specific Structures

The first stage of the Self-Discover approach focuses on discovering a task-specific reasoning structure through 3 steps: Select relevant reasoning modules, Adapt them to the specific task, and Implement them into a structured plan. This tailored plan serves as a guide for the LLM to follow in stage 2 to generate answers to task instances.

10:03

😊 Results: Self-Discover Significantly Enhances LLM Reasoning

Experiments showed Self-Discover significantly improves reasoning for LLMs like Palm 2.0 and GPT-4 across diverse tasks, achieving over 7% and 6% gains over baselines. It excels in world knowledge tasks and is highly efficient needing fewer inferences. Examples demonstrate how Self-Discover provides logical reasoning chains leading to correct answers.

😃 Advantages of Self-Discover: Flexibility, Blends Methods

Analysis shows all 3 Self-Discover steps contribute value in enhancing reasoning. The discovered structures work across models, proving flexibility. Self-Discover blends the strengths of various prompting approaches, outperforming specialized methods. Reasoning and planning abilities are crucial for LLMs, and Self-Discover advances them without needing labels.

Keywords

💡Large Language Models

The video focuses on the recent progress and capabilities of large language models (LLMs). These models are trained on vast amounts of textual data and use advanced transformer models to generate coherent text and solve complex problems. The video discusses various techniques aimed at enhancing the reasoning capabilities of LLMs.

💡Reasoning

A core theme of the video is enhancing reasoning abilities in LLMs. Reasoning refers to the higher-level cognitive process of drawing conclusions, inferences, judgments, interpreting concepts, solving problems etc. Improved reasoning allows LLMs to mirror human intelligence more closely.

💡Prompting Methods

The video introduces various prompting strategies that provide LLMs with a structure or hints to guide their reasoning and problem solving process. Methods like Chain of Thought and Plan-and-Solve prompts are discussed as ways to develop tailored approaches for specific tasks.

💡Self-Discover

Self-discover is the novel approach introduced in this work to have LLMs self-discover optimal reasoning structures for each unique task, leading to greater efficiency. It integrates strengths of different prompting techniques into a flexible framework.

💡Modularity

A key aspect of the self-discover approach is its modularity i.e. the reasoning process is broken down into atomic modules like 'reflective thinking', 'critical analysis' etc. which are then adapted and combined in a customized way for each task.

💡Task-Specific Structures

The self-discover method aims to uncover task-specific reasoning structures i.e. identify patterns of logic, knowledge and analytical steps uniquely suited to solve a particular task efficiently. This is inspired by human problem-solving.

💡Structured Reasoning

The self-discovered reasoning structures provide LLMs with an explicit, structured sequence of steps to tackle tasks. This structured reasoning guides models logically from problem to solution instead of relying solely on pattern recognition from training data.

💡Generalizability

Experiments reveal the broad generalizability of discovered reasoning structures across models, tasks and domains. This flexibility and wider applicability across contexts is a major advantage of the approach.

💡Performance Gains

Quantitative comparisons demonstrate significant performance gains in accuracy and efficiency from using self-discovered reasoning structures over other methods across mathematical, linguistic and commonsense reasoning tasks.

💡Interpretability

An additional benefit of structured logical chains is providing greater transparency and interpretability into the LLM's reasoning process instead of black-box solutions.

Highlights

Self-discover aims to uncover the unique reasoning structure for each task, leading to more efficient computation and outperforming other methods

Self-discover selects, adapts and implements reasoning modules to discover task-specific structures

Self-discover customizes general problem-solving approaches into something more task-specific

Self-discover turns adapted reasoning modules into a structured, step-by-step plan for solving the task

In stage 2, the model follows the implemented reasoning structure to generate answers

Self-discover improves reasoning over a wide range of complex tasks, surpassing state-of-the-art methods

Self-discover is particularly effective for tasks requiring broad world knowledge

Self-discover is more efficient than methods requiring multiple inference calls

Each step in self-discover contributes value, with full process providing the best improvement

Self-discovered structures work across models, showing flexibility and universal value

Self-discover blends prompting methods to create a versatile, powerful approach to reasoning

Self-discover doesn't need specific task labels, allowing flexibility in reasoning approaches

Self-discover enhances reasoning in smaller models like LLama and ChatGPT

Many benchmarks test reasoning abilities; self-discover advances prompting methods flexibility

Self-discover combines reasoning approaches without needing data, outperforming data-driven optimization

Transcripts

section

introduction we're diving into the world

of large language models llms which have

been making waves with their ability to

generate coherent texts and follow

instructions thanks to the power of

Transformers our goal is to push these

llms even further helping them to reason

and solve complex problems more

effectively to do this researchers have

been getting creative with different

prompting methods taking cues from how

we humans think and solve problems

for instance some methods mimic the way

we tackle problems step by step or how

we break down a big problem into smaller

more manageable

pieces there's even a method inspired by

how we step back to understand the

nature of a task better but there's a

catch each of these techniques is like a

standalone tool assuming one size fits

all for problem solving which isn't

always the case we believe that each

task has its own unique puzzle to solve

and under understanding that puzzle is

key to solving the task

efficiently take the least to most

prompting method for example it's been a

GameChanger for tasks that involve

manipulating symbols or putting things

together in new ways outperforming other

methods because it matches the tasks

inherent

structure in this paper we introduce

self-discover our approach to uncover

the unique reasoning puzzle each task

presents it's like how we as humans come

up with a game plan for tackling a new

problem we start with a toolbox of basic

problem solving strategies like breaking

things down or thinking

critically then without any Specific

Instructions we use these tools to

figure out a plan that makes sense with

the task at

hand this plan is our road map from

start to finish self-discover Works in

two main stages first it figures out the

task unique reasoning structure it does

this by guiding the llm through a series

of steps to generate a plan for the

task then in the second stage the llm

uses this plan to solve actual

problems this method has several

advantages it combines the strengths of

different problemsolving strategies it's

computationally efficient and it gives

us insights into the task in a way

that's easy to understand we put

self-discover to the test on 25 tough

reasoning tasks and saw some impressive

results it performed other methods in

most cases showing the power of a

tailored approach to problem

solving plus it was more efficient and

provided clearer insights into how the

tasks were

solved we also took a closer look at why

self-discover works so well by analyzing

different types of tasks we found that

it shines in areas requiring World

Knowledge and does pretty well with

algorithmic challenges

too this was further supported by

looking at where it stumbled mainly with

computation errors in math

problems finally we explored how

flexible self-discover reasoning

structures are by seeing if they could

be transferred between different

models this adaptability is promising

for future work in using structured

reasoning to tackle challenging problems

with

llms in essence self-discover is about

mimicking the human approach to problem

solving by using what we know to figure

out a plan then executing that

plan where excited about its potential

to make llms even smarter and more

capable problem

solvers section summary large language

models llms powered by Transformers have

made significant progress in generating

coherent texts and solving

problems to enhance ilm's reasoning

capabilities various prompting methods

inspired by human reasoning have been

proposed such as few shot and zero shot

Chain of Thought cut decomposition-based

prompting and step back

prompting however these methods have

limitations as they assume a universal

reasoning process for all

tasks the self-discover approach aims to

self-discover the unique reasoning

structure for each task leading to more

efficient computation and outperforming

other methods in solving challenging

reasoning

tasks section stage one self-discover

task specific structures in the first

part of our process we focus on

discovering task specific structures

through three main steps selecting

adapting and

implementing firstly we choose the

relevant reasoning modules needed to

solve a task from a pool of available

modules not every module is suitable for

every task for instance reflective

thinking might be useful for finding

basic scientific principles while

creative thinking could be better for

coming up with a new story

continuation next we customize the

chosen reasoning modules to fit the

specific task we're dealing with this

means changing a general problem-solving

approach like breaking down a problem

into smaller parts into something more

task specific such as solving arithmetic

operations in a certain order for math

problems this step involves rephrasing

the descriptions of the selected modules

to make them more relevant to the task

finally we turn these tailored module

descriptions into a structured plan that

outlines exactly what needs to be done

to solve the task

this plan is a step-by-step guide based

on the adapted reasoning

modules after creating the structured

plan we move to the second part of our

process where we use the plan to tackle

the

tasks we attach this plan to each task

instance and instruct models to follow

it to generate

answers this approach helps in providing

a clear method for solving the

tasks in our experiments we test our

method on a variety of challenging

reasoning tasks including complex

arithmetic natural language

understanding and tasks requiring World

Knowledge we use several Advanced

language models for this purpose and

compare our method to other approaches

that either directly prompt the model to

generate an answer or use a reasoning

process without our structured

approach our method stands out because

it bases the reasoning process on

specific Atomic reasoning modules and

guides the model with a clear structured

plan

we also explore how effective our method

is compared to others that use raw

reasoning modules in different ways such

as aggregating multiple outputs or using

the best output based on prior

knowledge additionally we look into

whether our structured reasoning

approach can maintain its Effectiveness

across different tasks and models

compared to Simply optimizing the

wording of

prompts this is important for

understanding if our method has a

broader applicability Beyond specific

tasks or models

section summary in the first stage of

the self-discover approach we undertake

three key actions select where relevant

reasoning modules are chosen based on

task examples adapt where the selected

reasoning modules are tailored to the

specific task at hand and Implement

where the adapted reasoning modules are

operationalized into a structured

actionable plan for solving the task

following this in the second stage the

implemented reasoning structure is

appended to all instances of the task

prompting models to follow the reasoning

structure to generate an answer the

approach is evaluated on diverse

reasoning benchmarks including

challenging tasks from Big bench a

grounded social agent reasoning task and

a subset of math tasks using

state-of-the-art language models and

comparing with other zero shot prompting

methods for

reasoning section

results we explored several questions

through our experiments to understand

the impact of discovering reasoning

structures on the reasoning abilities of

large language models llms specifically

focusing on which types of problems

benefit the most from self-discover and

its efficiency in boosting llm

performance we also provided examples to

illustrate how self-discovered

structures influence llm outputs

compared to other prompting

methods firstly we found that

self-discover significantly enhances the

reasoning capabilities of both palm 2L

and gp4 across a of complex reasoning

tasks When comparing self-discover to

other methods like direct prompting

Chain of Thought cut and plan and solve

PS We observed notable

improvements for instance on 23 tasks

self-discover led to a 7% and 6%

absolute improvement over cot and PS

respectively for Palm

2L similar improvements were seen with

gp4 specifically self discover

outperformed other methods in more than

20 out of 24

tasks in tasks related to social

understanding self-discover achieved

over 27% and 32% Improvement on Palm 2L

and GPT 4 respectively surpassing

previous state-of-the-art

methods for math related tasks the gains

were modest but still present our

analysis showed that self-discover is

particularly effective for tasks

requiring a broad range of world

knowledge such as understanding Sports

recommending movies or identifying

ruins this suggests that self-discover

strength lies in its ability to

integrate multiple reasoning modules

providing a more comprehensive approach

to reasoning that can capture Essential

Knowledge that might be missed by

simpler

methods in terms of efficiency

self-discover proved to be significantly

more efficient than other methods that

require multiple inference calls such as

self-consistency or majority voting

for example in a comparison using GPT 4

self-discover required far fewer

inference calls while still

outperforming other baselines in

accuracy this makes self-discover a

highly efficient method for enhancing

reasoning in llms on a large

scale we also shared qualitative

examples from Palm 2L showing how

self-discover adapts unique structures

for different reasoning tasks

integrating various reasoning modules to

provide clear insights on solving the

tasks in comparison to cot and plan and

solve self-discover led to more logical

conclusions and correct answers

demonstrating its Effectiveness in

guiding llms towards accurate

reasoning in summary our experiments

confirm that self-discover not only

improves llm reasoning across a wide

range of tasks but also does so

efficiently making it a valuable method

for enhancing llm

performance section summary the

experimental results demonstrate that

self-discover significantly enhances the

reasoning capabilities of palm 2L and

gp4 across various complex reasoning

tasks achieving notable improvements

over baselines like Chain of Thought and

plan and solve notably self-discover

excels in tasks requiring diverse World

Knowledge such as Sports understanding

and movie recommendation by integrating

multiple reasoning modules and

outperforming other prompting

methods moreover it achieves Superior

performance with significantly fewer

inference calls compared to other

methods making it an efficient and

effective reasoning enhancement approach

for large- scale

deployment section deep diving into

self-discovered reasoning structures

we've been diving deep into the world of

self-discover a method we developed to

enhance reasoning tasks in large

language models

llms after seeing how well it works we

wanted to understand more about its

components and the additional ADV

advantages it might offer we found that

the three steps we use in self-discover

which are select adapt and Implement are

all crucial for improving the model's

ability to reason from

scratch we also discovered that the

reasoning structures we come up with can

be applied across different models

showing their Universal

value for instance we could take

structures identified by one model like

Palm 2L and successfully use them in

another such as gp4 and even in llama 2

to

70b this not only proves the

effectiveness of our method but also its

flexibility to get into the specifics we

ran some tests to see what happens when

we tweak the self-discover

process we tried using just select then

select and adapt and finally all three

steps together the results were clear

each step added value with the full

process providing the best Improvement

in reasoning

tasks this shows that each action in

self-discover is important and

contributes to the model's enhanced

performance we also explored how well

our self-discover method as compared to

other

approaches for example we used it to

apply reasoning structures found by Palm

2L to gp4 and compared the results to

another method called

opr our method outperformed opr in most

cases which is impressive considering

opr uses data to optimize its prompts

while self-discover Works without

needing any data

upfront furthermore we looked at how

these self-discovered structures could

help smaller models like llama 2 and

chat GPT perform better on reasoning

tasks the results were promising showing

that our method could significantly

improve performance even outdoing other

Advanced prompting

techniques speaking of prompting methods

the landscape is quite diverse with many

different strategies like Chain of

Thought prompting and others each has

its strengths and weaknesses depending

on the task at hand what sets

self-discover apart is its ability to

blend these methods together creating a

more versatile and Powerful approach to

solving reasoning

tasks it's a bit like programming where

you use different basic elements to

write a

program self-discover does something

similar but with prompting

methods lastly we touched on the

importance of reasoning and planning in

the development of

llms many benchmarks and tasks are

designed to test these abilities and

various methods have been proposed to

enhance model

performance these methods often try to

mimic human reasoning patterns which can

be very effective but also

limited our self-discover method offers

a way to combine multiple reasoning

approaches seamlessly without needing

specific task

labels this is a big step forward as it

allows for more flexibility and

adaptability in how models approach and

solve reasoning

tasks