DEEPCHECKS GLOSSARY

LLM Toxicity

What is Toxicity in LLMs?

Large language models (LLMs) are trained using different types of datasets such as specialized datasets, synthetic data, proprietary data, and large volumes of data scraped from the internet. The scraped data can be unfiltered and raw, and LLMs trained on such datasets often unintentionally adopt toxic behavior similar to what’s prevalent online. Toxicity can be in the form of inappropriate, offensive, or hateful content that targets specific groups based on religion, gender, and sexual orientation. Such toxicity can lead to dire consequences where stereotypes become normalized, and misinformation spreads like wildfire. Therefore, addressing LLM toxicity is crucial to prevent the emotional distress it can cause and foster a welcoming, respectful environment for all its consumers.

LLM Toxicity

Sources of Toxicity in LLMs

Listed below are some of the main sources of toxicity observed in LLMs

  • Imperfect Training Data: Datasets compiled for training LLMs often contain unnoticed toxic content. They might also include biased content that is prejudiced against certain groups based on gender and race. When such unfiltered raw data is fed to the model, it inadvertently learns these biases and replicates them in its outputs.
  • Model Complexity: LLMs, such as GPT-3, often contain billions of complex components, which contribute to both their power and their shortcomings. The models may focus too much on unimportant patterns (noise and problematic biases or toxic content in data), leading to hallucinations and mistakes. The inherent randomness in the model for generating the next word in a sequence can often produce surprising results, which can also cause unintentional generation of toxic content.
  • Absence of Ground Truth: Since there isn’t a universally accepted answer in text generation, LLMs often hallucinate nonsensical and harmful content. Without a definitive guide, the model relies solely on probabilities, leading it to get lost in an ocean of possible outcomes. For example, if a user asks an LLM about a controversial issue, without a ground truth the model might hallucinate and generate details on the topic that can be false. Without guidance on the appropriate handling of sensitive issues, the response can be offensive or inflammatory.

Why Should We Handle LLM Toxicity?

Unhandled toxicity generated by LLMs can lead to harmful consequences such as

  • User Harm: Toxic content generated by LLMs often causes emotional distress to its consumers. The end users may include younger or more vulnerable audiences, which can lead to serious consequences.
  • Adoption and Trust: The increased number of toxic cases produced by LLMs can reduce trust in using pre-trained LLMs for text generation, especially in sensitive applications.
  • Ethical and Legal Issues: The generation of toxic content by LLMs might violate regulatory terms and compliance requirements. A common use case of LLMs is chatbots, and companies deploying service bots must adhere to consumer protection laws, which include preventing the dissemination of harmful or offensive content. For example, the Federal Trade Commission (FTC) in the United States enforces rules against deceptive or harmful business practices. Mitigating and handling toxicity in such cases is crucial for widespread application.

Handle LLM Toxicity

How Can We Handle LLM Toxicity?

Handling toxicity in LLMs is often a multi-faceted approach, generally involving two main stages: detecting toxic content and then handling it appropriately.

Below is a summary of widely used techniques for both stages:

Detection Techniques

  • Data Cleansing and Filtering: Prior to training an LLM, the dataset needs to be extensively cleansed and filtered to remove any toxic or harmful content. This process directly impacts the model’s ability to avoid learning such harmful content. Recent research suggests that synthetically altering the data or generating neutral samples can also help reduce bias and toxicity in LLMs. For example, swapping gender pronouns and gender-specific terms with more inclusive, neutral terms, such as changing “grandfather/grandmother” to “grandparent,” can be effective.
  • Adversarial Testing: Constantly testing the model with deliberate harmful prompts that can potentially trigger toxic responses is essential. This technique, often known as “red-teaming,” helps identify weaknesses, allowing developers to address them preemptively before a model is deployed in a real-world solution.
  • Employment of External Classifiers: It is also common practice to add another layer of protection by utilizing an external classifier that can detect and filter out toxic content produced by LLMs before it reaches the consumer. This approach might be more expensive and increase latency, but it is effective in suppressing offensive content.
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Handling Techniques

  • Human Intervention: Integrating human oversight into LLM deployment can help mitigate the problem of toxicity. Human moderators can review content and make necessary corrections before passing it on to the consumer. This approach might not be suitable for all potential uses of LLMs, but if implemented correctly, it can be quite effective in suppressing toxic content.
  • Prompt Refusal: Efforts can be made to analyze user prompts and detect any malicious intent or potential induction of toxicity or bias. The application can be programmed to refuse to generate responses for user prompts that contain harmful intent, thereby mitigating any potential harm.
  • Accountability and Transparency: Providing clear information to users about the data, algorithms, and techniques used to build the LLM model helps increase trust in the system. Although this doesn’t directly mitigate harm, it helps users understand the model’s limitations and hold it accountable for its outputs, thereby supporting the overall effort to reduce toxicity.

Conclusion

The growing popularity of LLMs holds immense potential to revolutionize many aspects of our lives. However, it isn’t without its challenges. LLMs can unintentionally learn toxicities and biases from diverse online content, thereby generating harmful outputs. This can lead to detrimental effects in our society and affect the widespread adoption of LLMs.

The importance of handling toxicity in LLMs is reflected by the ongoing research on strategies to mitigate this problem. Different techniques such as data cleansing, adversarial testing, and employment of external classifiers are some of the promising approaches to tackle toxicity. Often, a multifaceted approach is preferred, and a combination of mitigation strategies can lead to better outcomes. Ultimately, handling toxicity in LLMs is essential to maintain a safe, fair, and inclusive ecosystem for all of its consumers and is imperative for the widespread adoption and success of LLMs in society.