Perfecting LLMs to Mirror Human Preferences Accurately Through RLHF
RLHF for LLMs is an emerging technique to reduce bias and hallucination of AI model outputs. It has gained importance as a method for fine-tuning LLMs to build some of the powerful models, such as GPT by OpenAI and Claude by Anthropic.
Earlier, the challenge with LLMs was generating responses that, while grammatically correct, missed the intended meaning or tone. Without human input, models couldn’t fully grasp nuanced human preferences or respond within ethical boundaries.
Reinforcement Learning from Human Feedback (RLHF) refines LLM responses based on actual human insights through a feedback loop or reward model. RLHF shapes LLMs to deliver outputs that are coherent and ethically relevant text.
To learn what RLHF can do, we need to understand how a model such as ChatGPT is trained and how RLHF fits into this process.
Open AI’s Paper on Reinforcement Learning
In their 2017 paper publication “Deep Reinforcement Learning from Human Preferences,” OpenAI presented RLHF as a novel machine learning technique that concentrates on human preferences. Since then, this ground-breaking idea has spurred additional study and applications.
Importance of RLHF for LLMs
RLHF brings human oversight into the otherwise automated training process of LLMs. It is because traditionally, LLMs were trained on vast datasets using supervised or unsupervised learning methods. However, these methods lack direct feedback on how well the generated outputs align with human preferences.
RLHF for LLMs closes this gap with human insight by allowing them to rate or provide feedback on model outputs. This creates a more interactive training loop that helps the model adjust its responses. Thus, it refines the model’s performance of desirable output to align with human expectations.
Human-Centered Labeling Methods to Perfect LLMs
Recent interest in large language models (LLMs) has led to their increased application across various tasks. Reinforcement learning from human feedback (RLHF) is a crucial part of their training to align responses with user intentions. In this regard, research and developments for increasing AI model capabilities are regularly happening. These are:
- Bayesian Reinforcement Learning
Human oversight that has gained importance through Reinforcement Learning (RL) is prioritized via a Bayesian prior. Bayesian methods are used for model-based RL, where prior information can be shown on the parameters of the Markov model. Bayesian methods are essential because they allow the model to balance new data (i.e., human feedback) with prior knowledge and belief, enabling it to respond to human preferences even when data is limited or noisy.
2. Reward Modeling with Weak Supervision for Language Models
This method aggregates noisy feedback sources, such as crowdsourcing or semi-automated labeling, to label data for RLHF training. Although less precise than the Bayesian method, weak supervision can help establish general labeling patterns when human feedback is sparse.
3. Self-Labeling or Pseudo-Labeling
By using the LLM’s own predictions as interim labels, models may continue training on new data. It means self-labeling without the need for constant human insights. To keep the LLM on track, this method acts as a bridge between human feedback sessions instead of completely negating it.
4. Contrastive and Self-Supervised Learning
For LLMs, learning to differentiate between highly-rated and low-rated responses can help sharpen the model’s alignment with human preferences. This is particularly useful for refining language generation tasks where nuance is essential.
The development of large language models (LLMs) through reinforcement learning from human feedback (RLHF) has revolutionized intelligent conversational agents. However, LLMs still have a long way in mastering nuanced conversational skills, such as disambiguation, where models tend to make implicit guesses.. In task-specific contexts, limited access to high-quality conversational data impacts their ability to learn optimal dialogue action policies.
This is where data labeling and annotation companies have a role to play. Partnering with specialized annotation services ensures that LLMs benefit from human-in-the-loop approach, where real experts refine and guide the models’ learning processes. By providing carefully labeled, high-quality conversation samples, data labeling companies allow LLMs to improve their understanding of user intent, accurately identify ambiguities, and respond with more relevant and precise dialogue actions.
For companies looking to enhance the quality of their LLMs, partnering with data annotation experts is advisable.
The practical case of RLHF in InstructGPT
The objective of training GPT like Generative AI models was to predict upcoming text tokens in an accurate manner. But, this method did not ensure that the output was genuine, helpful, or safe.
Therefore, the role of RLHF is to address the alignment of LLMs, a process that guides the model towards outputs that reflect human values.
The following image (source) illustrates how RLHF is used in InstructGPT for our further understanding.
The image shows a three-step process for training a gen AI model using Reinforcement Learning from Human Feedback (RLHF). Here’s an explanation of each step:
Step 1: Collect Demonstration Data and Train a Supervised Policy
The first step is the collection of demonstration data and training of supervised policy. Here, data annotators provide ideal responses to various prompts. Through supervised learning, the language model (such as GPT-3) is improved by using the annotation of responses; in other words, it is trained to reflect human preferences.
Step 2: Collect Comparison Data, and Train a Reward Model
In the second step, comparison data is collected to build a reward model. After generating multiple possible responses to a given prompt, the annotator ranked the model outputs based on previous knowledge to employ criteria, such as helpfulness or accuracy. This ranked data is then used to train a reward model, which learns to predict the quality of responses based on the human-provided rankings.
Step 3: Optimize a Policy Against the Reward Model Using Reinforcement Learning
The third step involves optimizing the language model’s policy through reinforcement learning, specifically Proximal Policy Optimization (PPO). The model generates a response with each prompt, which the reward model evaluates and scores according to desired human preferences and guidelines. This reward is then used to adjust the model’s policy, encouraging it to create responses that are more likely to receive higher rewards in future interactions.
Since OpenAI introduced RLHF (Reinforcement Learning from Human Feedback), it has indeed become a game changer, leading many data annotation companies to offer RLHF as a specialized service. These annotation companies offer data collection, labeling, and feedback pipelines where domain experts evaluate model outputs to better align models with human preferences. Their service has become increasingly valuable as RLHF proves effective in refining AI model behavior to be more user-centered and reliable.
RLHF is perfect for use cases like content moderation, where humans are better at identifying language that qualifies as hate speech, bullying, and other negative conduct.
The Future of Human-Centric LLMs
As RLHF techniques continue to mature, LLMs will become increasingly capable of accurately mirroring complex human preferences. Data annotation companies understand the foundational role of quality data and may develop a more flexible RLHF methodology by combining Bayesian priors and other annotation techniques.
Having explored these paths, the goal is crystal clear i.e., to create AI models that are not only efficient but deeply aligned with human values and thoughts. RLHF for LLMs journey showcases the drive towards creating AI models that genuinely understand and interact on a human level.
