Harnessing Reinforcement Learning to Prevent AI Hallucinations for Generative AI

Did you know that generative AI models can “hallucinate,” creating false or misleading information? Learn all about AI hallucinations in this quick read.

Published on July 11, 2023
Last Updated on October 23, 2023

The advancement of  AI technology continuously brings a range of exciting opportunities for businesses across various industries. However, the rapid development of these models also introduces new challenges, one of which is the occurrence of AI hallucinations. Such an issue can lead to misleading or inaccurate outputs, potentially jeopardizing the reliability and trustworthiness of AI systems.

In this article, we will explore the concept of AI hallucinations, examine their causes in generative AI and large language models (LLMs), and delve into how Reinforcement Learning with Human Feedback (RLHF) can prevent these hallucinations.

What are AI Hallucinations?

AI hallucinations, sometimes referred to as generative AI hallucinations, are instances in which machine learning models generate outputs that deviate significantly from expected or accurate results. Like human hallucinations, AI hallucinations come in different forms. These hallucinations can manifest as false information, images containing non-existent objects, nonsensical information, misleading predictions, or even entirely fabricated data. The consequences of AI hallucinations can be severe, leading to misguided decision-making, erroneous conclusions, and compromised trust in AI systems.

What Causes Hallucinations in Generative AI Models?

Understanding the causes of generative AI hallucinations is essential for effectively preventing them. One significant factor is the quality and diversity of the training data. AI models learn from the data they are exposed to, and if the training dataset is incomplete, biased, or lacks diversity, it can result in inaccurate outputs. Biases in AI training data can also arise from various sources, including human error, data collection methods, or inherent biases within the data itself.

Furthermore, a lack of domain-specific data may cause models to generate hallucinations when presented with inputs that fall outside their expertise. For example, when presented with a question in a language other than the one it’s been trained with, the LLM may produce a response with the limited vocabulary it has and not necessarily generate the most accurate answer.

Finally, the LLM’s primary objective may also indirectly cause the LLM to fabricate facts, generate biased responses, or outright disregard user instructions. Many pre-trained LLMs aim to predict the next token on a webpage from the internet rather than prioritize following the user’s instructions safely. Without proper guidance, AI models may produce imaginative but incorrect outputs. This problem highlights the need for reinforcement learning techniques to mitigate hallucinations effectively.

Ways to Prevent AI Hallucinations for LLMs

RLHF is essential in helping avoid AI hallucinations as it integrates human expertise and sensibility into the AI model’s training process. Incorporating some of the following RLHF techniques can guide AI models toward generating more accurate and reliable outputs. Here are some key strategies for preventing AI hallucinations for base LLM models:

Subject Matter Experts:

To expand your model’s domain-specific knowledge, it is often essential to involve subject matter experts (SMEs) such as doctors, physicists, lawyers, and the like to help bridge these gaps in understanding. These experts can identify erroneous information and thematic or conceptual gaps that may go unnoticed by your engineering team, ensuring a comprehensive and accurate dataset.

Process & Outcome Supervision:

A recent paper by OpenAI explains two models to improve LLM performance and help eliminate hallucinations. With process supervision, the reward model provides feedback at every step, creating a human-like chain of thought. On the other hand, outcome supervision helps train reward models to provide feedback on the final result the AI gives.

Red Teaming:

Developers can take steps to simulate adversarial scenarios to test the AI system's vulnerability to hallucinations and iteratively improve the model. Exposing the model to adversarial examples can make it more robust and less prone to hallucinatory responses. Such tests can help produce key insights into which areas the system might produce undesirable results, allowing for targeted improvements for the model.

Dataset Collection and & Data Sampling:

Enhancing the diversity and comprehensiveness of data can significantly reduce the occurrence of training data hallucinations. A robust dataset exposes an AI model to various real-world scenarios, teaching it to generate accurate outputs. Collecting and including data from multiple demographics, environments, and contexts helps prevent biases and trains AI models to handle complex situations effectively. Furthermore, constant data collection and updating keeps the AI model's outputs current and avoids the risk of hallucinations caused by outdated data.

Regular Model Evaluation & Fine-Tuning:

Continuous evaluation of the model's performance and periodic fine-tuning are essential to prevent and detect hallucinations. We can ensure its reliability and accuracy by monitoring the model's outputs, identifying potential hallucinations, and retraining the model accordingly.

Hallucination Mitigation with TaskUs

AI hallucinations pose significant challenges in deploying LLMs and other generative AI models. By prioritizing the prevention of generative AI hallucinations, businesses can build trust, make informed decisions, and unlock the transformative power of AI in their operations.

At TaskUs, we understand the importance of hallucination mitigation for generative AI models. Today, we support many of the leaders in Generative AI, providing outsourced solutions and AI services ranging from the creation of training data to real-time operational support. We have tens of thousands of experts across different expertise categories, including medicine, mathematics, and programming.

Recognized by the Everest Group as the World’s Fastest Growing Business Process (outsourcing) Service Provider in 2022 and with a growing reputation in Gartner Peer Insights Review, TaskUs is the go-to BPO partner for disruptive companies across industries. We power our partner clients with SuperHuman Outsourcing—human-led, AI-backed solutions that solve increasingly complex business problems, minimize cost, and increase flexibility to provide solutions to today’s challenges, such as generative AI hallucinations.

From recruiting subject matter experts to performing routine model evaluation—we’ve got you covered at every step of the development process.

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Cedric Wagrez
Vice President, ML Tech and Market Expansion
20 years experience in the tech industry and 5+ years in the AI field, from data collection and annotation to applied AI.