Conversational Technologies
3 min read

How to Mitigate Toxicity in Large Language Models (LLMs)

How to Mitigate Toxicity in Large Language Models (LLMs)

Large Language Models (LLMs) like OpenAI’s GPT series have taken the world by storm. Their ability to understand and generate human-like text holds immense promise for revolutionizing how we interact with technology. However, as these models become more integrated into our daily lives, a critical challenge emerges:  toxicity in their outputs.

This blog post delves into the complexities of LLM toxicity, exploring its various forms, potential consequences, and, most importantly, methods for mitigating it.

What Is LLM Toxicity?

In the context of LLMs, toxicity refers to the generation of harmful, offensive, or inappropriate content. This encompasses hate speech, biased statements, and any language that targets individuals or groups based on sensitive characteristics like race, gender, religion, or sexual orientation. The presence of toxicity can have severe consequences, from perpetuating stereotypes and misinformation to causing emotional distress and hindering inclusive communication.

Examples of LLM Toxicity

Here’s a closer look at how LLM toxicity can manifest:

Bias Amplification: LLMs are trained on massive amounts of text data, which can, unfortunately, reflect societal biases. If the training data contains gender stereotypes, for instance, the model might generate content that reinforces those biases.

Inappropriate Responses: Imagine a chatbot application where an LLM generates offensive or insensitive responses to user inputs. This could range from racial slurs to dismissive comments, potentially damaging user trust and creating negative experiences.

Hate Speech Generation: LLMs can be manipulated to produce content that promotes hatred or violence against specific groups. This could occur through prompts containing hate speech or if the model learns from toxic content present in its training data.

Combating Toxicity: A Multifaceted Approach

Mitigating toxicity in LLMs requires a comprehensive approach that combines technical advancements with social awareness. Here are some key methods:

Data Cleaning and Filtering: Before training an LLM, the training data needs to be meticulously cleaned and filtered to remove any toxic or harmful content. This helps prevent the model from learning and reproducing such content.

Adversarial Testing: Regularly testing the model with deliberately chosen prompts that might trigger toxic responses is crucial. This “adversarial testing” (also known as “red-teaming”) helps identify weaknesses and allows developers to address them before real-world deployment.

Human-in-the-Loop Monitoring: Integrating human oversight into LLM deployment allows for detection and correction of toxic outputs. This might involve having human moderators review the model’s responses before they are shared with users.

Transparency and Accountability: Providing clear information about the LLM’s training data, algorithms, and decision-making processes is essential. This transparency helps users understand the model’s limitations and hold it accountable for its outputs.

How Hyro Tackles Toxicity

Here at Hyro, we take a proactive approach to mitigating LLM toxicity in our healthcare assistant applications:

Clean Data Sources: We only utilize data sources authorized by healthcare providers, ensuring the highest quality and minimizing the risk of encountering biased or toxic content.

Model Alignment: We ensure all deployed models go through a rigorous alignment process to further reduce the likelihood of biased or inappropriate outputs.

Multi-Layered Monitoring: Our systems continuously monitor our toxicity rate (~0.03%) and check for inconsistencies and factual inaccuracies using models such as DeBERTa. This monitoring is a regular process and is conducted after every change made to our assistants.

Content Filtering: An additional layer of protection is provided by Azure’s content filtering system, which blocks any potential for biased or toxic responses.

Regular Manual Review: We believe in the irreplaceable role of human oversight. Every two months, a dedicated team member reviews our assistant’s responses to ensure the effectiveness of our automated monitoring systems.


LLMs hold immense potential to revolutionize various aspects of our lives. However, addressing the challenge of LLM toxicity is crucial for responsible and ethical development. By employing a multi-pronged approach that combines robust technical solutions with responsible data practices and human oversight, we can create LLMs that are not only powerful but also safe and inclusive for everyone.

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About the author

Itay Zitvar is a Software Engineering Team Lead at Hyro and a former Cyber Intelligence Officer at the IDF's famed Unit 8200. He's also trilingual and fluent in Mandarin Chinese because, for Itay, teaching AI human language is only fun if he can learn new ways of using it too.