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Artificial intelligence (AI) frameworks and AI chatbots rely heavily on machine learning. Machine learning uses mathematical formulas and datasets to learn new information with minimal or no supervision. A bridging mechanism then translates the data into contextualized interactions. This is where reinforced learning from human feedback (RLHF) comes into play.

What is human feedback training?

Advanced algorithms play a key role in teaching large language models (LLMs) to converse naturally with users. They use coding to analyze patterns and identify relationships. That’s a logical-based task that any database can complete. It isn’t a human way of thinking. Machine Learning (ML) through reinforcement learning is more effective in training LLMs to think and respond like humans.


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Additionally, it’s essential to have open discussions surrounding AI features that mimic human traits, as a lack of transparency can lead to mistrust and suspicion among the public. As we continue to incorporate human-like capabilities into technology, ethical considerations must remain at the forefront of development and implementation processes.

Think of how a newborn learns to speak and understand language. A baby doesn’t know how to interpret complex algorithms. They learn through trial and error, with constant feedback from their parents or caregivers. Similarly, reinforcement learning involves feedback from humans. LLMs, exposed to real conversations, learn and improve their responses in a human-like manner through trial and error.

Source: Pexels

How does trial and error RLHF operate?

RLHF is another AI buzzword, like neural networks and machine learning. What is reinforced learning, and how does it transform data into meaningful interactions shaped by human feedback?

Let’s say that neural networks have an infinitely more capable intellect. They’re virtual and composed of billions of lines of raw data, so they need more input. A reward and penalty system is in place, with a feedback loop created as responses generate more data. As the loop is continually run through, the machine learning process gains a more refined understanding of the context of a conversation.

This works for query and answer-based feedback loops, all the way to nuanced conversations, which require a human touch. Anything less causes the textual version of the “uncanny valley” to be felt on the human end because the responses from LLMs may seem almost human-like but fall short of the real thing. The RLHF loop goes like this:

1. The human user makes a query or introductory sentence as an input statement.
2. An initial action is created as a response by the neural network.
3. Here’s the crux of the feedback loop engine. The human receives the response and evaluates it for natural context.
4. Depending on the industry applications or business-specific usage scenario, the human is a knowledgeable company insider who can fill in supplied prompts accurately and naturally.
5. Explicit responses are made as scores or perhaps as a thumbs up, assuming the feedback is positive. A thumbs down, a negative response, is interpreted as a penalty.
6. Implicit responses are a little more complex. As a conversation progresses, algorithms note the natural flow of the conversation, assigning scores to preferred conversational branches. Dead-end responses are marked with negative scores.
7. The points assigned to the responses engendered by the human input are used to update the language model and improve its natural communication skills.

This human feedback mechanism is a real-time loop. Positive feedback and negative, reward and penalty. All the data slowly shape the language model, refining it and training it to interact naturally.

Why is reinforcement learning important?

No matter the specialism of a language model, its goal is to mimic a real person. This can be seen in ChatGPT’s chatbot functions. After every input, you’ll see a thumbs-up and a thumbs-down icon. Two dialog boxes appear when you hover over them. The thumbs-up icon indicates a good response, while the thumbs-down icon is a bad response.

This is a tiny example of explicit RLHF at work. ChatGPT asks for further input when its response is penalized with a thumbs down. Be prepared to type your reasons for downvoting its output.

It’s an important part of the learning experience. Machine learning models scrape data and employ algorithms, but they also encode human feedback as a means of cognitive training. In effect, you’re shaping a machine’s artificial personality and its capacity for natural language. As it learns, it acts more human. The next user does the same so that continual iterations of the feedback loop produce a chatbot or virtual agent that sounds like a mature individual, someone who fosters trust in a brand, business, or company presence.


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Will machine learning models and LLMs develop super-human intellects because of continual iterations of the reward-penalty feedback loop? That’s an unlikely scenario. If anything, the chatbots and virtual agents will take on more human qualities, short of calling it quits when 5 p.m. comes around.

No dataset scraped from the internet, nor human interactions found on a social engineering website, can mimic humans, not without some amount of unnatural, stilted speech that sounds like the computer off of the Starship Enterprise. For natural conversations, RLHF is the most promising approach.

How to benefit from machine learning and LLMs

How else can users gain a satisfactory relationship with machines? They can’t feel, can’t enjoy music, or taste food. These are human activities. Knowledge of those biologically rooted responses is locked inside a real brain. The only way Machine Learning and artificial intelligence can glean this knowledge and become sentient is to learn from you.

Rather than being questioned by a robot, providing real-time feedback is more pleasing, as made possible by reinforcement learning. With reinforcement learning, machines are taught to enhance natural language conversations.


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