By
Serena
Wellen
|
Senior
Director
of
Product
Management,
LexisNexis
The
introduction
of
Generative
Artificial
Intelligence
(Gen
AI)
tools
specifically
designed
for
the
legal
profession
is
stirring
animated
conversations
about
the
potential
for
these
tools
to
transform
the
way
that
law
is
practiced,
but
perhaps
less
understood
is
how
the
technology
making
these
tools
possible
is
getting
better
and
more
reliable.
Gen
AI
describes
Large
Language
Models
(LLMs)
that
are
designed
to
create
new
content
in
the
form
of
images,
text,
audio
and
more.
This
is
the
category
of
AI
from
which
emerged
ChatGPT,
the
model
launched
in
November
2022 that
brought
Gen
AI
into
the
cultural
mainstream.
The
initial
model,
GPT-2,
was
built
on
1.5
billion
parameters
of
data
inputs.
The
subsequent
model,
GPT-3,
was
built
on
175
billion
parameters
and
GPT-4
may
have
been
built
on
an
astonishing
170
trillion
parameters.
But
as
staggering
as
this
rapid
growth
is,
the
truth
is
that
LLMs
may
have
peaked
in
size.
Indeed,
OpenAI’s
Sam
Altman
has
indicated that
“the
age
of
giant
AI
models
is
already
over”
and
that
future
versions
will
improve
in
different
ways.
Of
course,
the
early-stage
versions
of
these
LLMs
produced
some
results
that
amazed
legal
professionals
with
their
possibilities,
and
other
results
that
alarmed
them
because
of
the
risks.
But
over
the
course
of
the
past
year,
there
has
been
tremendous
innovation
in
LLM
technology
that
is
clearly
driving
Gen
AI
in
the
right
direction.
For
one
thing,
the
gap
between
private
models
(e.g.,
those
from
OpenAI,
Google,
Anthropic,
Microsoft,
etc.)
and
open
source
models
(e.g.,
Llama,
Falcon,
Mistral,
etc.)
is
narrowing.
This
is
important
because
the
open
source
ecosystem
is
driving
a
huge
amount
of
innovation,
fueled
by
easier
access
to
the
models
themselves,
easier
availability
of
training
data
sets
for
everyone,
lower
costs,
and
the
worldwide
sharing
of
research
to
guide
further
development.
Second,
prompt
engineering
has
evolved
to
the
point
where
it
is
much
more
akin
to
traditional
software
engineering.
In
the
early
days
of
Gen
AI,
the
data
science
behind
creating
the
back-end
prompts
to
guide
the
models
was
untested,
and
few
software
engineers
had
the
requisite
training
or
experience.
We
now
have
a
variety
of
tools
—
such
as
LangChain
and
PromptFlow
—
that
are
very
similar
to
other
tools
and
templates
regularly
used
in
software
engineering,
making
it
easier
for
developers
to
create
Gen
AI
applications
at
scale.
Third,
LLMs’
ability
to
reason
and
to
minimize
“hallucination”
has
become
quite
impressive
with
the
proper
techniques.
One
of
these
techniques
is
known
as
Retrieval
Augmented
Generation
(RAG).
The
RAG
model
is
an
LLM
prompt
cycle
that
accesses
information
external
to
the
model
to
improve
its
response
to
specific
queries,
rather
than
only
relying
upon
data
that
was
included
in
its
training
data.
ChatGPT,
for
example,
relies
solely
on
its
training
data:
information
extracted
from
the
open
web
(an
unknown
number
of
which
may
not
be
grounded
in
fact).
The
most
advanced
applications
of
the
RAG
approach,
such
as
how
we
use
RAG
within
our
Lexis+
AI
platform,
can
now
deliver
accurate
and
authoritative
answers
that
are
grounded
in
the
closed
universe
of
authoritative
content
—
in
our
case,
the
most
comprehensive
collection
of
case
law,
statutes
and
regulations
in
the
legal
industry.
“With
the
right
model
training,
source
materials
and
integration,
RAG
is
poised
to
mitigate,
if
not
resolve,
some
of
generative
AI’s
most
troubling
issues,”
reported
Forbes.
Another
important
dimension
of
tech
innovation
with
LLMs
is
that
more
organizations
are
now
deploying
a
“multi-model”
approach
to
building
their
Gen
AI
solutions.
This
shift
away
from
placing
big
bets
on
a
single
LLM
is
enabling
developers
to
leverage
different
benefits
from
different
models,
creating
their
own
solutions
in
a
more
flexible
way
that
maximizes
functionalities
and
minimizes
risks.
And
an
interesting
development
to
keep
your
eyes
on
in
the
year
ahead
is
the
potential
evolution
of
LLMs
with
something
called
Large
Agentic
Models
(LAMs).
LAMs
are
advanced
systems
that
can
perform
tasks
and
make
decisions
by
interfacing
with
other
human
users
or
other
automated
tools.
Unlike
traditional
AI
systems
that
respond
to
user
prompts,
LAMs
are
designed
to
understand
their
environment
and
take
actions
to
achieve
their
assigned
goals
without
direct
human
intervention,
according
to
TechTarget.
But
perhaps
the
most
important
technology
innovation
with
LLMs
for
legal
professionals
is
that
data
security
and
privacy
safeguards
are
being
placed
front
and
center
with
the
newest
tools
in
development.
Secure
cloud
services
are
more
readily
available,
data
sanitation
and
anonymization
are
standard
in
training
models,
encryption
is
more
reliable
than
ever,
access
controls
are
vastly
improved,
and
there
are
sound
data
governance
protocols
around
the
retention
of
prompt
inputs
and
response
outputs.
At
LexisNexis,
we
have
followed
a
product
development
plan
that
embraced
Gen
AI
technology
in
a
deliberate
manner
so
we
can
capture
the
upside
of
these
tools
developed
specifically
for
the
legal
domain,
while
mitigating
the
potential
risks
associated
with
the
first
generation
of
the
open
Web
Gen
AI
tools,
such
as
ChatGPT.
Lexis+
AI
is
our
breakthrough
Gen
AI
platform
that
is
transforming
legal
work
by
providing
a
suite
of
legal
research,
drafting,
and
summarization
tools
that
delivers
on
the
potential
of
Gen
AI
technology.
Its
answers
are
grounded
in
the
world’s
largest
repository
of
accurate
and
exclusive
legal
content
from
LexisNexis
with
industry-leading
data
security
and
attention
to
privacy.
By
saving
time
with
Lexis+
AI
enabled
tasks,
legal
professionals
have
more
time
to
do
the
work
only
they
can
do.
In
fact,
our
customers
have
reported
time
savings
of
up
to
11
hours
per
week
using
Lexis+
AI.
To
learn
more
or
to
request
a
free
trial
of
Lexis+
AI,
please
go
to
www.lexisnexis.com/ai.
Chris
