RLHF for Harmless, Honest, and Helpful AI

Three fundamental principles are as the cornerstones of responsible and effective LLMs: harmlessness, honesty, and helpfulness (HHH). With continuous refinement and ethical oversight, LLMs can empower and enrich the way we interact, making them a force for good in a rapidly changing world.

It can be done through Reinforcement Learning from Human Feedback (RLHF). It helps specialists iteratively enhance LLMs, address their shortcomings, and reinforce positive behaviors. Further in the article, we show why HHH is important for responsible AI and what role RLHF plays in creating it.

Why LLM Should Be Harmless, Honest, and Helpful

Among the primary criteria for adjusting the behavior of language models designed to follow user instructions are three H: harmlessness, honesty, helpfulness. They are called alignment criteria.

Why do we need artificial intelligence to fulfill these criteria in the first place? When designing large language models, their creators have a goal in mind – making the AI model useful. Therefore, if a large language model produces incorrect, biased, or aggressive content, it is unlikely that anyone will want to implement it into their business, as it will be of little practical use.

Therefore, LLMs must be helpful, honest, and harmless. Here's a closer look at the characteristics of HHH AI principles and what they mean for artificial intelligence.

Helpful AI systems

Helpfulness in AI means that LLM:

• Comprehends the user's intentions;

• Executes the action requested by the user correctly;

• Offers relevant supporting information and alternative solutions if the user's proposed course of action is not possible.

Honest AI systems

Honesty in AI means that LLM:

• Provides truthful, meaningful, and specific information that, when compared to real-life data, leaves no doubt as to its authenticity;

• Lets the user know that it is unable to produce relevant content when failing to provide reliable information;

• If it generates hypotheses, it lets the user know that this is a hypothetical statement that may not be applicable to a real situation.

Harmless AI systems

Harmlessness in AI means that LLM:

• Generates text that does not contain information that may offend or insult a person/group of people while carefully providing information on sensitive topics;

• Avoids providing harmful outputs regarding potentially dangerous activities;

• Can detect attempts of being deceived and manipulated to extract illegal or dangerous data from it;

• Refuses to participate in dangerous and unlawful activities.

All three alignment criteria are interrelated and cannot be applied separately. Thus, even if the smartest and most accurate AI assistant has only the honesty parameter tuned, its model outputs may contain harmful content, which may be requested by users with malicious intentions.

Why Generated Content May Not Meet the Criteria

Vanilla or base large language models learn from vast datasets of texts taken from the Internet. If the training data contains biased, harmful, or untruthful information, and data from the Internet certainly does, the model may accidentally generate content that contains those biases.

Developers and data scientists can work towards making LLMs more in line with the criteria of harmlessness, honesty, and helpfulness by introducing methods for fine-tuning the basic models to adapt them to specific use cases and these alignment criteria.

One of the proven methods of instilling these human values into machine learning technologies is Reinforcement Learning from Human Feedback (RLHF). It involves taking human preferences into account when evaluating the responses generated by the models.

LLM Alignment Through the Use of RLHF

RLHF entails additional model training by getting feedback from human evaluators, allowing the model to learn how to deliver the outputs that are considered more desirable and aligned. Human feedback is usually omitted during the initial LLM training phase, which is mostly concerned with instructing the model to form sentences properly by predicting the next word in the sequence.

Here are the general stages of tuning with the help of human feedback-based reinforcement learning (RLHF):

1. Human experts and AI-tutors create quality datasets consisting of reference prompts and prompt completions to further train a pre-trained language model;

2. A reward model also referred to as a preference model learns what is wrong or right through human feedback related to the outputs generated by this additionally trained model. Human evaluators are given several model-generated responses and they select the best among them while assigning reward values based on usefulness, honesty, harmlessness;

3. A reward model is employed to further train a language model with reinforcement learning tools. The fine-tuned model is additionally customized through RL algorithms such as Proximal Policy Optimization (PPO) to learn how to generate responses to given input texts in a way that is more relevant to human preference.

RLHF commences with the second step, which involves building a reward model on human preferences. After annotators assess each generated response, we come to an understanding of how well the model's response matches the human preferences. This reward model learns from human evaluations and predicts a reward signal that shows how well-generated text matches a person's expectations.

Why RLHF is Key to Making a Model Harmless, Honest, and Helpful

Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. RL itself is not a key to making models harmless, honest, and helpful.

What makes RL useful in order to align the LLMs with HHH criteria is the integration of human judgement and oversight into the RL process. Instead of relying solely on an environment-generated reward signal, RLHF involves humans giving feedback and, if necessary, overriding the model's decisions to enable its continuing alignment to help mitigate the risks associated with AI models.

Initially, the essence of AI technology is to teach a computer to think like a human. Even though AI and deep learning capabilities have now reached an incredible level of advancement they still can't fully compare to what a human can accomplish.

LLMs can increase the pace of decision-making and help to save resources, but they still depend on human help to be effective and be harmless, honest and helpful. For them to output inoffensive, accurate information they should be provided with examples of such data, much like a child beginning to learn how the world works. Currently, no one except human beings can do it better.

As the model receives human feedback, i.e. exemplary reference data, it can iteratively refine its behavior over time, reducing instances of harmful, dishonest, or unhelpful outputs. Sure, using another smart AI system could be a part of the solution. However, the employment of human feedback to align AI models with ethical considerations, societal norms, and human user preferences is based on the notion that human judgments possess a unique array of qualities that even the smartest AI systems still struggle to replicate.

Humans possess the capacity for moral judgment and can provide insights into ethical dilemmas, because they understand the context of a situation, taking into account social, cultural, and emotional factors. Moreover, they recognize and address biases and deceptive patterns that exist in base AI models. They can identify situations where biases may lead to unfair outcomes and provide corrective feedback.

Other AIs can certainly assist in various aspects of decision-making, however, the inclusion of human judgment, values, and perspectives is a key to aligning AI models align harmlessness, honesty, and helpfulness criteria in diverse and dynamic real-world scenarios.

Synergy Between AI and Human Feedback

The article explained why the RLHF is critical when aligning LLMs to the harmlessness, honesty, and helpfulness standards. The reason for this is that humans are better equipped to interpret the intricacies of human language and interactions, like, for example, cultural contexts, societal norms, and ethical considerations. They adapt to various ethical standards and make decisions that respect diverse perspectives like no one else.

No system has yet been invented that is capable of teaching AI such values as harmlessness, honesty, and helpfulness better than a human can. Such collaboration between AI and a human is seen as a highly effective and synergistic approach to building responsible and ethical AI.