Three ways RLHF is advancing large language models
The evolution of large language models (LLMs) is happening at an exciting pace. Breakthrough generative ai models like ChatGPT, GPT-4 and Google Bard, among others, are revolutionizing natural language processing. One of the key factors responsible for these advancements is a training method known as reinforcement learning from human feedback (RLHF).
RLHF is an approach to machine learning training that goes a considerable step beyond traditional reinforcement learning. During RLHF, an advanced LLM learns to make decisions not only through feedback from its environment, but also by receiving feedback from humans.
By training LLMs to produce more accurate output, RLHF has a significant impact on performance. Read on to explore how RLHF differs from traditional reinforcement learning, the steps involved in RLHF training and the way it's being used to improve LLMs.
Reinforcement learning versus RLHF
Traditional reinforcement learning is a type of training that helps create intelligent computer programs through trial-and-error, feedback and a system of rewards. The artificial intelligence (AI) model takes an action (or doesn't), affecting its environment, which transitions to a new state and returns a reward. That feedback signal of rewards is how reinforcement learning models change their actions — they take the steps that maximize rewards.
Although reinforcement learning uses feedback from the environment, the AI model doesn't necessarily learn the best possible behavior. This is because it's extremely challenging to design a good reward system. For example, consider a model being trained to play chess. Once the machine learns the reward system, it can often find loopholes, and, as a result, perform tasks that don't contribute to its original training objective. Incorporating human feedback can prevent this from happening by providing input that keeps the machine focused on its training objective. Further, with traditional reinforcement learning, the model essentially has free reign to try a multitude of combinations that don't necessarily contribute to its end objective. Human intervention can prevent the machine from trying these wasteful combinations that won't result in achieving its training objective, thereby speeding up the training process significantly.
Incorporating the experience and real-world knowledge of humans via feedback helps the model output better responses. It also enables the model to respond to more complex human preferences and produce more accurate output. Take OpenAI's ChatGPT, for example. After its initial introduction, it made headlines because of its impressive performance. One of the primary reasons? RLHF techniques were extensively applied to its training.
How RLHF works
You can break down how RLHF works into several steps. First, a pre-trained LLM is fed a dataset of human-written prompts and responses. The model uses this dataset to predict an output based on received inputs.
After receiving a prompt, the LLM outputs a response, which humans rank by quality and accuracy. This feedback is used to create a reward model for reinforcement learning.
Finally, the model is fine-tuned by incorporating the human-generated reward signals. During this iterative process, the RLHF model continues to learn from human feedback to further improve its performance.
Benefits of RLHF
The incorporation of human feedback leads to LLMs with improved natural language understanding. As a result, these models demonstrate improved accuracy, a reduction in biased algorithms and a decrease in hallucinations.
1. Better performance
AI models trained with RLHF can provide better answers than those that learn through reinforcement learning alone. By having human feedback incorporated into the training process, the model learns to better understand complex human preferences. Taking these preferences into account, the model can provide responses that are not only more accurate and coherent, but are also more contextually appropriate and more closely aligned with human preferences.
2. Bias reduction
RLHF helps to reduce the occurrence of biased algorithms, a significant move forward for AI. Not only do biased algorithms affect the accuracy of your model, they can also lead to marginalization of certain groups of people.
With RLHF, models are repeatedly refined through the process of collecting human feedback. By having a diverse group of human trainers evaluate and rank the model-generated outputs, they can identify and address any biased behavior to ensure the model's outputs are more aligned with the interests of the collective, rather than one particular group.
3. Reduced hallucinations
Generative AI models can have a propensity to hallucinate, a phenomenon where they provide responses that incorporate fabricated data that appears authentic. Essentially, models fill in knowledge gaps with plausible-sounding words or phrases that are actually inaccurate or nonsensical.
Incorporating human feedback into the model training process through RLHF is a valuable way to reduce hallucinations. The human feedback can be used to provide corrective input to the model, and even teach the model to learn to say that it cannot answer a question with certainty.
The road ahead with RLHF
RLHF is a practical approach to AI training that is significantly improving the performance of LLMs. It enables AI models to better understand and adapt to complex human preferences, leading to more accurate output that is better aligned with human values. In addition, this training technique is helping to reduce bias in AI models and decrease hallucinations.
This is an exciting time for AI. While no one is certain what the future holds, one thing is for sure: As AI models continue to evolve, so will the training methods. Reach out to our team of AI experts to learn how we can help you advance your large language models.
