SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures
Summary
TLDRLarge 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
- π LLMs have made progress in generating text and solving problems, but more reasoning capabilities are needed
- π§ Researchers use prompting methods inspired by human reasoning to enhance LLM reasoning
- π Self-discover aims to uncover the unique reasoning structure for each task
- π Self-discover has 3 key steps: select, adapt and implement reasoning modules
- π Self-discover outperforms other methods on challenging reasoning tasks
- π‘ Self-discover is efficient, requiring fewer inference calls
- π Self-discover is effective for tasks requiring world knowledge
- βοΈ All 3 self-discover steps contribute to enhanced LLM performance
- π Self-discover blends prompting methods into a versatile approach
- π€ 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
π 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.
π 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.
π 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.
π 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.
Mindmap
Keywords
π‘Large Language Models
π‘Reasoning
π‘Prompting Methods
π‘Self-Discover
π‘Modularity
π‘Task-Specific Structures
π‘Structured Reasoning
π‘Generalizability
π‘Performance Gains
π‘Interpretability
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
Browse More Related Video
Can LLMs reason? | Yann LeCun and Lex Fridman
3.3 | INDUCTIVE VS DEDUCTIVE REASONING | MATHEMATICS IN THE MODERN WORLD | ALOPOGS
5 MINUTES AGO: OpenAI Just Released GPT-o1 the Most Powerful AI Model Yet
OpenAI Releases GPT Strawberry π Intelligence Explosion!
Recent breakthroughs in AI: A brief overview | Aravind Srinivas and Lex Fridman
Deductive Vs Inductive Vs Abductive [Reasoning in Research, Concept, Difference, Examples]
5.0 / 5 (0 votes)