Introduction to Generative AI (Day 2/20) How are LLMs Trained?
Summary
TLDRThe video script explains the training process of language models (LMs) in three phases. Initially, LMs undergo language-free training to learn language structure by predicting missing words in sentences. Next, in instruction fine-tuning, LMs are provided with tasks and expected answers to improve their understanding and performance. Finally, reinforcement learning refines responses based on human preferences, using rewards to align the LM's output with what is liked by humans.
Takeaways
- 🧠 Language models (LMs) are trained through a process that helps them learn from patterns in text data and update their neural network parameters.
- 📚 Phase one of training is 'language model training', where the LM is fed sentences with missing words to learn the language structure.
- 🔍 In the 'instruction fine-tuning' phase, LMs are given instructions and expected answers to improve their task performance.
- 🏆 The third phase, 'reinforcement learning', refines the LM's responses to align with human preferences by using rewards for preferred answers.
- 🤖 Initially, LMs act as 'fill in the blank' machines, learning the structure of language before moving on to more complex tasks.
- 📝 The training process involves three distinct phases, each building on the previous to enhance the LM's capabilities.
- 📈 Reinforcement learning uses a scoring system to guide the LM towards generating responses that are more appealing to humans.
- 🔧 The LM updates its parameters based on feedback from the scoring system to better match human preferences.
- 📈 The training process is iterative, with each phase allowing the LM to become more adept at understanding and generating language.
- 🤝 Human feedback plays a crucial role in the final phase, shaping the LM's responses to be more relatable and engaging.
- 🌟 The ultimate goal of LM training is to create models that can understand and perform tasks in a way that is natural and pleasing to humans.
Q & A
What is the primary process through which language models learn?
-Language models learn through a process called training, which involves feeding them large amounts of text data so they can learn patterns and update their neural network parameters.
What are the three training phases of modern language models?
-The three training phases are language model training, instruction fine-tuning, and reinforcement learning.
What is the purpose of language model training phase?
-In the language model training phase, models are taught to understand and generate language by predicting missing words in sentences, helping them learn the language structure.
How does the instruction fine-tuning phase differ from the language model training phase?
-During instruction fine-tuning, the model is provided with instructions and expected answers, allowing it to learn how to perform tasks and understand instructions better.
What is the main goal of the reinforcement learning phase?
-The reinforcement learning phase focuses on refining the model's responses to align with human preferences by assigning scores or rewards based on how well the answers match human liking.
Why is reinforcement learning important in the training of language models?
-Reinforcement learning is important because it helps the model generate responses that are more appealing to humans, by identifying patterns that receive higher rewards.
What is the initial state of a language model after language model training?
-After language model training, the model is essentially a 'fill in the blank' machine, not yet ready to perform tasks effectively.
How does a language model update its neural network parameters during training?
-The model updates its neural network parameters by comparing its generated answers with expected outputs and adjusting accordingly to minimize errors and align with human preferences.
What role do human preferences play in the reinforcement learning phase?
-Human preferences guide the reinforcement learning phase by providing a metric for scoring or rewarding the model's responses, which helps the model learn to generate more likable answers.
Can you provide an example of an instruction that might be given to a language model during the instruction fine-tuning phase?
-An example of an instruction could be 'Can you answer this question for me?' followed by the provision of the expected answer for the model to learn from.
How does the language model's understanding of instructions improve over the course of training?
-The model's understanding improves as it is repeatedly exposed to instructions and their expected answers, allowing it to refine its neural network parameters and perform tasks more accurately.
Outlines
🤖 Language Model Training Phases
This paragraph explains the three stages of training for modern language models (LMs). Initially, in the 'language model training' phase, LMs are exposed to a vast array of sentences with missing words, prompting them to predict these omissions and thereby learning the language's structure. The second phase, 'instruction fine-tuning', involves providing the LM with instructions and expected answers, allowing it to generate responses and refine its neural network parameters to better understand and execute tasks. Finally, 'reinforcement learning' is introduced to align the LM's responses with human preferences by assigning rewards based on the likability of the generated content, which the LM uses to further optimize its output.
Mindmap
Keywords
💡LLMs (Large Language Models)
💡Training
💡Language Modeling
💡Pattern Recognition
💡Instruction Fine-Tuning
💡Reinforcement Learning
💡Neural Network Parameters
💡Human Preferences
💡Fill in the Blank
💡Task Performance
💡Expected Outputs
Highlights
LLMs undergo a training process involving large amounts of text data.
Training helps LLMs learn patterns and update neural network parameters.
There are three main training phases for modern LLMs.
Phase one is language model training without specific language instruction.
In language free training, LLMs predict missing words in sentences to learn language structure.
After language free training, LLMs are still basic 'fill in the blank' machines.
Phase two is instruction fine-tuning with provided instructions and expected answers.
LLMs improve by comparing generated answers with expected outputs in instruction fine-tuning.
Phase three is reinforcement learning to refine responses according to human preferences.
Reinforcement learning involves scoring answers based on alignment with human liking.
LLMs adjust parameters to align with higher rewarded generation patterns.
Training phases are crucial for LLMs to perform tasks effectively.
Understanding instructions is a key part of LLM training.
The goal of training is to develop LLMs that can generate meaningful and preferred responses.
Human feedback plays a significant role in the reinforcement learning phase.
LLMs learn to generate responses that are not only correct but also engaging.
Reinforcement learning helps in aligning LLM responses with human preferences.
The training process is iterative, with continuous updates to neural network parameters.
LLMs are designed to mimic human-like understanding and response generation.
Training phases are essential for the development of advanced language models.
Transcripts
we already discussed that llms undergo a
process called training which basically
involves feeding them with large amounts
of Text data so that they can learn from
the patterns update their neural network
parameters and generate meaningful
responses right now all modern nlms use
three training faces which are Super
intuitive to understand phase one is
called language free trining where they
taught how to understand and generate
language and how that's done is by
feeding them a large number of sentences
but skipping out some words or phrases
and allowing them to predit these missed
words the idea here is that they start
learning the language structure right
but even after this they're not yet
ready to perform tasks and they're
basically just fill in the blank
machines that's when we move on to phase
two called instruction fine-tuning in
this stage the llm is provided with a
bunch of instructions for instance can
you answer this question for me or can
you summarize this paragraph for me and
you also provide it with the expected
answers or ideal answers and it
basically generates these answers for
the instructions checks with the
expected output and again updates neural
network parameters and gets better at
understanding instructions and
Performing them so now that we have an
LM that can perform tasks for us but
what if the llm generates something
that's boring or you know generates
responses that humans don't really like
that's why we have phase three called
reinforcement learning this phase
basically focuses on refining the lm's
responses to align with human
preferences it's very similar to phase
two but instead of providing exact
outputs for the model to learn from in
this stage you basically assign scores
or rewards based on how well these
answers match with human liking right
using these rewards the LM identifies
which generation pattern receive higher
rewards versus lower rewards and updates
its parameters accordingly to align with
what we humans like
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