There is a lot of buzz surrounding large language models (LLM) today.
The world’s collective attention has been captured by the amazing things we have seen from ChatGPT and other similar applications– creative storytelling, demystification of complex topics, and even accurate and executable code blocks.
Many companies are exploring the idea of using AI & machine learning operationalization (MLOps) software to deploy these language models for tasks like answering customer queries, enhancing product functionality, or supercharging employee productivity.
While businesses explore these opportunities, they should also acknowledge risks that accompany adoption– the accuracy of the LLM outputs and information security for sensitive inputs (primarily when handled by a third party).
One way to address these concerns is for companies to train their own LLMs, custom-tailored to their use case and handled by their systems when necessary. If you are one of these companies considering creating your own LLM, one of the requirements to get there is a training process called reinforcement learning with human feedback (RLHF).
Reinforcement learning with human feedback (RLHF) is the process of pretraining and retraining a language model using human feedback to develop a scoring algorithm that can be reapplied at scale for future training and refinement.
As the algorithm is refined to match the human-provided grading, direct human feedback is no longer needed, and the language model continues learning and improving using algorithmic grading alone.
With tools like MLOps software, you can organize, track, and integrate RLHF models effectively, allowing for seamless model iteration and comparison.
Creating a usable and unique LLM for your business requires training that model in the specific subject matter and the expected outputs it will need to generate to successfully perform the job you’re giving it. It’s easy to imagine why a language model built to write creative stories would require different criteria from one intended to generate code snippets. Training the model properly is key to generating outputs that reliably match the intention.
Before 2022, AI was already impressive tech, but it lacked accessibility and widespread adoption. Technology constraints like processing power, worldwide internet access, and cloud technologies limited the practical applications of AI to just a few niche companies who could benefit from the investment of on-premise creation and hosting of an AI model. At that time, the limited scope of capabilities and expectations enabled relatively simple mathematical models to train new language models directly. Some AI models today continue to rely on this approach, but the advent of ChatGPT changed that forever.
OpenAI created the first ever RLHF model in 2019 called lm-human-preferences. This breakthrough enabled them to train GPT efficiently and robustly on a massive scale of information. This success enabled the company to release the fastest-growing internet app of all time.
While the current abilities of ChatGPT and other interfaces are impressive, we are just scratching the surface of what these technologies can do. As customer demand for feature parity fuels the infrastructure investment to give these systems even more horsepower, training models will likewise need to keep pace to realize the value of that investment.
Training an LLM with RLHF requires numerous steps and interactions. To help simplify the process, we broke this down into four major steps.
The pretraining phase for a new LLM typically involves feeding the model with a substantial amount of human-written text. This stage is referred to as unsupervised learning. This process helps the language model understand common phrases, grammatical structures, and alternate meanings of words in a language.
During this stage, the pre-trained model determines the mathematical probability that a given word will appear next in a string of text based on all the words that came before it and begins to understand conditions that will change the probability of which word will come next.
Now that the model has some existing context on language and some knowledge of facts based on its unsupervised learning, it's ready to respond to some initial questions, also known as prompts. As different prompts are provided to the model, some responses will be accurate and appropriate based on the initial training dataset, but others will be incorrect or off-topic. That’s the beauty of AI systems; they learn from the feedback they receive.
To determine which responses are good and which are bad, the model requires feedback for each response it generates, a process called supervised learning. Sourcing a large enough team of humans to provide this feedback at the scale LLMs require has historically been the most expensive part of developing a usable AI.
While developing the original GPT technology, scientists at OpenAI realized they would never have the time or resources to train their model in the way they wanted using human values alone. To overcome this challenge, OpenAI decided to build a learning algorithm that would mimic the prior human feedback the language model had received.
When creating this algorithm, human supervision was applied to the reward model output rather than the output of the language model itself. Over time the reward function gained sophistication and started to reliably match the grade that a human would provide for the same output. The creation of this algorithm allowed the GPT model to iterate responses and receive accurate feedback at a pace previously thought to be impossible.
Once a language model has been trained on the basics of language interactions using the above process, supervised fine-tuning begins. During this stage, the model is trained for its specific use cases with specific training data.
For example, if a language model is being created for customer service interactions, it needs to be trained on the meaning of terms relative to the product or service it supports, compared to a general text used for pretraining. RLHF can be leveraged for both the pretraining and fine-tuning stages.
If you are exploring the applications of LLMs for your business, taking the time to understand the RLHF training models can save you time and money. Some of the models below are available as open-source solutions that you can use or modify, while others can provide valuable clues into the mindset and strategies of leading AI researchers and creators.
The model that started it all. OpenAI developed lm-human-preferences in 2019 for GPT2 and successfully trained 774M parameters. The accompanying blog post documents the wins, challenges, and lessons learned from this early approach.
While this may not be the best training model to replicate to train your LLM due to its age and the improvements that have happened since it was introduced, it is a priceless window into the thinking and learning that went into its creation. A can’t-miss reading for any company considering creating their own RLHF model.
TRLX was developed by CarperAI, a group that “aims to democratize the instruction-tuning of large language models, the same way Stable Diffusion democratized image generation.”
TRL stands for transformer reinforcement learning. This model simplifies the reinforcement learning aspect of the fine-tuning language models from the human preferences method cited above, enabling researchers to concentrate on the critical decision-making aspects of reinforcement learning rather than dealing with the repetitive code necessary for distributed training.
This model was developed by the Allen Institute for AI, a “non-profit research institute founded in 2014 with the mission of conducting high-impact AI research and engineering in service of the common good.”
RL4LMs stands for reinforcement learning for language models and aims to solve the three challenges of existing feedback models. This includes training instability of RL algorithms when used across different applications of language in different settings, situations where the model assigns a passing score but a human does not consider the answer satisfactory, and finally, dealing with the variance that occurs in natural language processing (NLP) metrics.
InstructGPT is the latest RLHF model from OpenAI and is now the default model used in their API. OpenAI says, “InstructGPT models are much better at following instructions than GPT-3. They also make up facts less often and show small decreases in toxic output generation.”
InstructGPT is not open source. However, OpenAI has released other models and tools that are open source, such as GPT-3.5 and the OpenAI API. These resources can be used by developers and researchers for various NLP tasks.
ILQL stands for implicit language Q-learning and was developed by a Berkeley Ph.D. student named Charlie Snell. According to Snell’s site, “ILQL is an effective, easy-to-use offline RL method for language task learning. ILQL combines the strong utility optimization power of dynamic programming based offline RL methods with supervised learning's ease of use.”
This method uses Q-learning, which is described as an algorithm or method as opposed to a model. This allows simplified training of LLM for specific applications compared to the more robust models listed above, which are intended to train more broad language models.
RLHF has played a pivotal role in developing advanced language models like GPT-4. It offers several benefits, including improved performance, adaptability to different tasks, reduced biases, continuous improvement, and enhanced safety in AI systems. However, significant challenges remain.
RLHF models are subjected to certain limitations, such as:
There is room for improvement in addressing the limitations of RLHF models, which can lead to greater effectiveness and reliability in their application. By doing so, researchers can expand the potential applications for these models.
Future research in this field will focus on automation, addressing subjectivity, and ensuring long-term alignment with human values in evolving AI systems.
While the landscape of RHLF models is certain to evolve, the training models mentioned above form the foundation of current language models that revolutionized the technology landscape across countless software categories.
Whether you're building a language model as an entrepreneur, a diligent professional, or a hobbyist, it's crucial to embrace RLHF. By understanding the intricacies of RLHF training models, you can craft an LLM that captivates users with its unrivaled utility and reliability.
Learn more about machine learning models and how to train them.
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