Transfer Learning vs. Fine Tuning LLMs: Key Differences

The
two
most
prominent
techniques
that
define
the
functionalities
of

large
language
models
or
LLMs
include
fine-tuning
and
transfer
learning.
Each
technique
is
useful
for
pre-trained
large
language
models.
Before
diving
into
the
transfer
learning
vs
fine-tuning
debate,
it
is
important
to
note
that
both
approaches
help
users
leverage
the
knowledge
in
pre-trained
models.

Interestingly,
you
must
note
that

transfer
learning
is
also
a
type
of
fine-tuning,
and
the
best
way
to
explain
it
is
to
consider
it
full
fine-tuning.
Even
if
they
are
interconnected,
transfer
learning
and
fine-tuning
serve
distinct
objectives
for
training
fundamental
LLMs.
Let
us
learn
more
about
the
differences
between
them
with
detailed
impression
of
the
implications
of
both
techniques.

Definition
of
Transfer
Learning

The
best
way
to
find
answers
to
“What
is
the
difference
between
transfer
learning
and
fine-tuning?”
involves
learning
about
the
two
techniques.
Transfer
learning
is
an
important
concept
in
the
use
of
large
language
models
or
LLMs.
It
involves
the
use
of
pre-trained
LLMs
on
new
tasks.
Transfer
learning
leverages
the
existing
pre-trained
LLMs
from
LLM
families
such
as
GPT,
BERT,
and
others
who
were
trained
for
a
specific
task.

For
example,
BERT
is
tailored
for
Natural
Language
Understanding,
while
GPT
is
created
for
Natural
Language
Generation.
Transfer
learning
takes
these
LLMs
and
tailors
them
for
a
different
target
task
with
prominent
similarities.
The
target
task
can
be
a
domain-specific
variation
of
the
source
task. 

The
primary
objective
in
transfer
learning
revolves
around
using
the
knowledge
obtained
from
the
source
task
to
achieve
enhanced
performance
on
target
tasks.
It
is
useful
in
scenarios
where
you
have
limited
labeled
data
to
achieve
the
target
task.
You
must
also
note
that
you
don’t
have
to
pre-train
the
LLM
from
scratch. 

You
can
dive
deeper
into
the
transfer
learning
vs.
fine-tuning
comparison
by
accounting
for
the
training
scope
in
transfer
learning.
In
transfer
learning,
only
the
latter
layers,
including
the
parameters
of
the
model,
are
selected
for
training.
On
the
other
hand,
the
early
layers
and
the
related
parameters
are
frozen
as
they
represent
universal
features
such
as
textures
and
edges.

The
training
method
used
in
transfer
learning
is
also
known
as
parameter-efficient
fine-tuning
or
PEFT.
It
is
important
to
note
that
PEFT
techniques
freeze
almost
all
the
parameters
of
the
pre-trained
parameter.
On
the
other
hand,
the
techniques
only
implement
fine-tuning
for
a
restricted
set
of
parameters.
You
must
also
remember
that
transfer
learning
involves
a
limited
number
of
strategies,
such
as
PEFT
methods.

Excited
to
learn
the
fundamentals
of
AI
applications
in
business?
Enroll
now
in
the AI
For
Business
Course

Working
Mechanism
of
Transfer
Learning 

The
most
important
highlight
required
to
uncover
insights
on
the
fine-tuning
vs.
transfer
learning
debate
refers
to
the
working
of
transfer
learning.
You
can
understand
the
working
mechanism
of
transfer
learning
in
three
distinct
stages.
The
first
stage
in
the
working
of
transfer
learning
involves
identification
of
the
pre-trained
LLM.
You
should
choose
a
pre-trained
model
that
has
used
a
large
dataset
for
training
to
address
tasks
in
a
general
domain.
For
example,
a
BERT
model.

In
the
next
stage,
you
have
to
determine
the
target
task
for
which
you
want
to
implement
transfer
learning
on
the
LLM.
Make
sure
that
the
task
aligns
with
the
source
task
in
some
form.
For
example,
it
could
be
about
classification
of
contract
documents
or
resumes
for
recruiters.
The
final
stage
of
training
LLMs
through
transfer
learning
involves
performing
domain
adaptation.
You
can
use
the
pre-trained
model
as
an
initial
point
for
target
task.
According
to
the
complexity
of
the
problem,
you
might
have
to
freeze
some
layers
of
model
or
ensure
that
they
don’t
have
any
updates
to
associated
parameters. 

The
working
mechanism
of
transfer
learning
provides
a
clear
impression
of
the
advantages
you
can
find
with
it.
You
can
understand
the
fine-tuning
transfer
learning
comparisons
easily
by
considering
the
benefits
of
transfer
learning.
Transfer
learning
offers
promising
advantages
such
as
enhancements
in
efficiency,
performance,
and
speed.

You
can
notice
how
transfer
learning
reduces
the
requirement
of
extensive
data
in
the
target
task,
thereby
improving
efficiency.
At
the
same
time,
it
also
ensures
a
reduction
of
training
time
as
you
work
with
pre-trained
models.
Most
importantly,
transfer
learning
can
help
achieve
better
performance
in
use
cases
where
the
target
task
can
access
limited
labeled
data.

Identify
new
ways
to
leverage
the
full
potential
of
generative
AI
in
business
use
cases
and
become
an
expert
in
generative
AI
technologies
with Generative
AI
Skill
Path

Definition
of
Fine-Tuning 

As
you
move
further
in
exploring
the
difference
between
transfer
learning
and
fine-tuning,
it
is
important
to
learn
about
the
next
player
in
the
game.
Fine-tuning
or
full
fine-tuning
has
emerged
as
a
powerful
tool
in
the
domain
of
LLM
training.
Full
fine-tuning
focuses
on
using
pre-trained
models
that
have
been
trained
using
large
datasets.
It
focuses
on
tailoring
the
models
to
work
on
a
specific
task
through
continuation
of
the
training
process
on
smaller,
task-centric
datasets.

Working
Mechanism
of
Fine-Tuning

The
high-level
overview
of
the
fine-tuning
for
LLMs
involves
updating
all
model
parameters
using
supervised
learning.
You
can
find
better
clarity
in
responses
to
“What
is
the
difference
between
transfer
learning
and
fine-tuning?”
by
familiarizing
yourself
with
how
fine-tuning
works.

The
first
step
in
the
process
of
fine-tuning
LLMs
begins
with
the
identification
of
a
pre-trained
LLM.
In
the
next
step,
you
have
to
work
on
determining
the
task.
The
final
stage
in
the
process
of
fine-tuning
involves
adjusting
weights
of
pre-trained
model
to
achieve
desired
performance
in
the
new
task. 

Full
fine-tuning
depends
on
a
massive
amount
of
computational
resources,
such
as
GPU
RAM.
It
can
have
a
significant
influence
on
the
overall
computing
budget.
Transfer
learning,
or
PEFT,
helps
reduce
computing
and
memory
costs
with
the
frozen
foundation
model
parameters.
PEFT
techniques
rely
on
fine-tuning
a
limited
assortment
of
new
model
parameters,
thereby
offering
better
efficiency.

Take
your
first
step
towards
learning
about
artificial
intelligence
through AI
Flashcards

How
is
Transfer
Learning
Different
from
Fine
Tuning?

Large
Language
Models
are
one
of
the
focal
elements
in
the
continuously
expanding

artificial
intelligence
ecosystem.
At
the
same
time,
it
is
also
important
to
note
that
LLMs
have
been
evolving,
and
fundamental
research
into
their
potential
provides
the
foundation
for
new
LLM
use
cases. 

The
growing
emphasis
on
transfer
learning
vs.
fine-tuning
comparisons
showcases
how
the
methods
for
tailoring
LLMs
to
achieve
specific
tasks
are
major
highlights
for
the
AI
industry.
Here
is
an
in-depth
comparison
between
transfer
learning
and
fine-tuning
to
find
out
which
approach
is
the
best
for
LLMs.

• 
  Working
  Principle

The
foremost
factor
in
a
comparison
between
transfer
learning
and
fine-tuning
is
the
working
principle.
Transfer
learning
involves
training
a
small
subset
of
the
model
parameters
or
a
limited
number
of
task-specific
layers.
The
most
noticeable
theme
in
every
fine-tuning
vs.
transfer
learning
debate
is
the
way
transfer
learning
involves
freezing
most
of
the
model
parameters.
The
most
popular
strategy
for
transfer
learning
is
the
PEFT
technique.

Full
fine-tuning
works
on
a
completely
opposite
principle
by
updating
all
parameters
of
the
pre-trained
model
over
the
course
of
the
training
process.
How?
The
weights
of
each
layer
in
the
model
go
through
modifications
on
the
basis
of
new
training
data.
Fine-tuning
brings
crucial
modifications
in
the
behavior
of
a
model
and
its
performance,
with
specific
emphasis
on
accuracy.
The
process
ensures
that
the
LLM
precisely
adapts
to
the
specific
dataset
or
task,
albeit
with
consumption
of
more
computing
resources.

The
difference
between
transfer
learning
and
fine-tuning
is
clearly
visible
in
their
goals.
The
objective
of
transfer
learning
emphasizes
adapting
the
pre-trained
model
to
a
specific
task
without
major
changes
in
model
parameters.
With
such
an
approach,
transfer
learning
helps
maintain
a
balance
between
retaining
the
knowledge
gained
during
pre-training
and
adapting
to
the
new
task.
It
focuses
on
minimal
task-specific
adjustments
to
get
the
job
done.

The
objective
of
fine-tuning
emphasizes
changing
the
complete
pre-trained
model
to
adapt
to
new
datasets
or
tasks.
The
primary
goals
of
fine-tuning
LLMs
revolve
around
achieving
maximum
performance
and
accuracy
for
achieving
a
specific
task.

Want
to
understand
the
importance
of
ethics
in
AI,
ethical
frameworks,
principles,
and
challenges?
Enroll
now
in
the Ethics
Of
Artificial
Intelligence
(AI)
Course

• 
  Architecture 

You
can
also
differentiate
fine-tuning
from
transfer
learning
by
learning
how
they
affect
model
architecture.
The
answers
to
“What
is
the
difference
between
transfer
learning
and
fine-tuning?”
emphasize
the
ways
in
which
transfer
learning
works
only
on
the
existing
architecture.
It
involves
freezing
most
of
the
model
parameters
and
fine-tuning
only
a
small
set
of
parameters.

Full
fine-tuning
changes
the
parameters
of
the
LLM
completely
to
adapt
to
the
new
task.
As
a
result,
it
would
involve
a
complete
update
of
the
model
architecture
according
to
emerging
requirements. 

• 
  Training
  Process

The
differences
between
fine-tuning
and
transfer
learning
also
focus
on
the
training
process
as
a
crucial
parameter.
Transfer
learning
involves
training
only
a
new
top
layer
while
maintaining
other
layers
in
a
fixed
state.
The
fine-tuning
transfer
learning
debate
frequently
draws
attention
to
the
freezing
of
model
parameters
in
transfer
learning.
Only
in
certain
cases
does
the
number
of
newly
trained
parameters
account
for
only
1%
to
2%
of
the
weights
of
the
original
LLM.

The
training
process
of
fine-tuning
LLMs
emphasizes
the
modification
of
specific
layers
and
parameters
for
carrying
out
the
new
tasks.
It
involves
updating
the
weights
of
any
parameter
according
to
the
emerging
utility
of
LLMs.

Want
to
learn
about
ChatGPT
and
other
AI
use
cases?
Enroll
now
in
the ChatGPT
Fundamentals
Course

• 
  Domain
  Similarity 

Another
factor
for
comparing
transfer
learning
with
fine-tuning
is
the
similarity
between
source
task
and
the
target
task
domain.
Transfer
learning
is
the
ideal
pick
for
scenarios
when
the
new
task
domain
is
almost
similar
to
the
original
or
source
task
domain.
It
involves
a
small
new
dataset
that
utilizes
the
knowledge
of
the
pre-trained
model
on
larger
datasets. 

Fine-tuning
is
considered
more
effective
in
scenarios
where
the
new
dataset
is
significantly
large,
as
it
helps
the
model
learn
specific
features
required
for
the
new
task.
In
addition,
the
new
dataset
must
have
a
direct
connection
with
the
original
dataset. 

• 
  Computing
  Resources 

The
discussions
about
the
transfer
learning
vs.
fine-tuning
comparison
draw
attention
to
the
requirement
of
computing
resources.
Transfer
learning
involves
limited
use
of
computational
resources
as
it
is
a
resource-efficient
approach.
The
working
principle
of
transfer
learning
focuses
on
updating
only
a
small
portion
of
the
LLM.

It
needs
limited
processing
power
and
memory,
thereby
offering
the
assurance
of
faster
training
time.
Therefore,
transfer
learning
is
the
ideal
recommendation
for
scenarios
where
you
have
to
train
LLMs
with
limited
computational
resources
and
faster
experimentation.

Fine-tuning
works
by
updating
all
model
parameters.
As
a
result,
it
requires
more
computational
resources
and
consumes
more
time.
Fine-tuning
utilizes
more
processing
power
and
memory
alongside
increasing
the
training
times,
which
increases
for
larger
models.
Full
fine-tuning
generally
needs
a
large
amount
of
GPU
RAM,
which
piles
up
the
costs
for
the
process
of
training
LLMs. 

Develop
expert-level
skills
in
prompt
engineering
with
the Prompt
Engineer
Career
Path

Final
Words 

The
comparison
between
fine-tuning
and
transfer
learning
helps
in
uncovering
the
significance
of
the
two
training
approaches.
You
have
to
find
important
highlights
in
the
fine-tuning
vs.
transfer
learning
comparison,
as
they
are
crucial
tools
for
optimizing
LLMs.
Transfer
learning
and
fine-tuning
can
help
in
tailoring
large
language
models
to
achieve
specific
tasks,
albeit
with
crucial
differences.
An
in-depth
understanding
of
the
differences
between
fine-tuning
and
transfer
learning
can
help
identify
which
method
suits
specific
use
cases.
Learn
more
about

large
language
models
and
the
implications
of
fine-tuning
and
transfer
learning
for
LLMs
right
now.