Prompt Tuning vs Fine‑Tuning for LLMs
Key Points
- Foundation models such as large language models are massive, pre‑trained systems that can flexibly handle tasks ranging from legal analysis to poetry generation.
- Fine‑tuning has traditionally been used to specialize these models, but it demands thousands of labeled examples and high computational cost.
- Prompt tuning (or prompt engineering) lets users steer a pre‑trained model toward a specific task by providing carefully designed textual cues or learned “soft” prompts, eliminating the need for extensive retraining.
- Simple tasks can be addressed with a few human‑crafted prompt examples, whereas more complex tasks increasingly rely on AI‑generated soft prompts embedded in the model’s representation layer.
Full Transcript
# Prompt Tuning vs Fine‑Tuning for LLMs **Source:** [https://www.youtube.com/watch?v=yu27PWzJI_Y](https://www.youtube.com/watch?v=yu27PWzJI_Y) **Duration:** 00:08:33 ## Summary - Foundation models such as large language models are massive, pre‑trained systems that can flexibly handle tasks ranging from legal analysis to poetry generation. - Fine‑tuning has traditionally been used to specialize these models, but it demands thousands of labeled examples and high computational cost. - Prompt tuning (or prompt engineering) lets users steer a pre‑trained model toward a specific task by providing carefully designed textual cues or learned “soft” prompts, eliminating the need for extensive retraining. - Simple tasks can be addressed with a few human‑crafted prompt examples, whereas more complex tasks increasingly rely on AI‑generated soft prompts embedded in the model’s representation layer. ## Sections - [00:00:00](https://www.youtube.com/watch?v=yu27PWzJI_Y&t=0s) **Prompt Tuning vs Fine‑Tuning** - The passage explains how prompt tuning offers a data‑light, energy‑efficient alternative to traditional fine‑tuning for adapting large language models to specialized tasks. ## Full Transcript
large language models like chat GPT are
examples of foundation models large
reusable models that have been trained
on vast amounts of knowledge on the
internet and they're super flexible the
same large language model can analyze a
legal document or write a poem about my
soccer team but what if we want to
improve the performance of pre-trained
llms or large language models to address
a specialized task well until recently
the best way to do that was using a
method that is known as
fine
tuning now with fine-tuning you gather
and label examples of the target task
lots and lots of examples and you fine
tune your model rather than train an
entirely new one from scratch but
there's a simpler far more energy
efficient technique that has emerged in
place of fine tuning and that is known
as
prompt
tuning so what is prompt tuning well
prompt tuning allows a company with
limited data to tailor a massive model
to a very narrow task and there's no
need for Gathering thousands of labeled
examples like we have to do with fine
tuning impr promp tuning the best cues
or front-end prompts are fed to your AI
model to give it task specific context
the promp prompts can be extra words
introduced by a human or more commonly
an AI generated number introduced into
the model's embedding layer to guide the
model towards a desired decision or
prediction now if all this sounds a
little bit familiar using prompts to
guide the output of an llm that's
because it most certainly is that is an
example of something else called
prompt
engineering now prompt engineering is
the task of developing prompts that
guide an llm to perform specialized
tasks honestly it sounds like a lot of
fun I think I'd quite like to be a
prompt engineer one day so if I want my
llm to specialize as an English to
French language translator I might
engineer a prompt to do so so my prompt
might start with let's say translate and
we want to translate
English to
French that's the task description of my
prompt then I'm going to add some few
short examples so let's let's add those
now so here's the English word bread
into the French word p that's one that's
one shot example let's add another
butter we're going to turn that into B
and then the next part of my prompt I'm
going to add the word that I wanted to
translate next so
cheese and that's it now prompts like
this written by a human me Mr wannabe
prompt engineer himself they Prime the
model to retrieve the appropriate
response from the llms vast memory in
this case specifically for other words
in French and the model's output is its
prediction what is this model going to
Output
fromage that work worked we've used
prompt engineering to train a model to
perform a specialized task with just a
single prompt introduced at inference
time without needing the model to be
retrained but if the task is more
complex than this it may require dozens
of these prompts and so these
handcrafted prompts have begun to be
replaced by AI designated prompts known
as soft prompts
now soft prompts have been shown to
outperform human engineered prompts
which we can know now as hard prompts
these were hardcoded by a
human this is not good news for my
prompt engineering career because you
see unlike hard prompts AI designed soft
prompts they're used in prompt tuning
and they are unrecognizable to the human
eye each prompt consists of an embedding
or strings of numbers that distill
Knowledge from the larger model and
these soft prompts can be high level or
task specific and act as a substitute
for additional training data and
Incredibly effective in guiding the
model towards the desired output but do
keep in mind that one drawback of prompt
tuning and soft prompts in general is
its lack of interpretability that means
that the AI discovers prompts optimize
for a given task but often can't explain
why it chose those embeddings like deep
learning models themselves soft prompts
are opaque all right so let's consider
we have here a
pre-trained model so this might be a
large language model something like
that okay now let's consider three
options for tailoring this pre-trained
model for specialization
and I'm going to talk about the three
that we've covered here so first of all
fine
tuning fine
tuning so with fine tuning we take this
pre-train model and we supplement it we
supplement it specifically with tunable
examples these are the thousands of
examples that I talked about right at
the beginning once we've done that we
can then provide our input data into the
model
and it should now be able to perform our
specialization so that's fine-tuning
what about
prompt engineering how is that different
well with prompt engineering we take the
model as it is we haven't tuned it and
then we add in an additional prompt so
we have our input
prompt but we also add into that input
prompt an engineer
prompt which sits in front of it so we
effectively provide two prompts here for
the
specialization that's that's what we did
with our language translator so we
provided this pre-written engineered
prompt and then we provided our input
which was
cheese so that's prompt engineering what
about prompt tuning how is that
different well with prompt tuning again
we use the pre-trained model as it is
and we again provide an
input but we also
provide something in front of that input
and that is the tunable
soft prompt that is generated by the AI
itself and it's the combination of these
two things that allow us to use the
model in a specialized way promp tuning
is proving to be a game changer in a
variety of areas for instance in
multitask learning where models need to
quickly switch between tasks researchers
are finding ways to create Universal
prompts that can be easily recycled
techniques like multitask prompt tuning
allow the model to be adapted swiftly
and for a fraction of the cost of
retraining prompt tuning is also showing
promise in the field of continual
learning where AI models need to learn
new tasks and Concepts without
forgetting the old ones essentially
prompt tuning allows you to adapt your
model to specialized tasks faster than
find tuning and prompt engineering
making it easier to find and fix
problems my career as a prompt engineer
might be over before it started so I
guess it's back to the drawing board or
rather back to the embedding layer
because in the AI World a string of
numbers is worth a thousand
words if you have any questions please
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watching