Artificial Intelligence (AI) systems have made remarkable strides in recent years, with applications ranging from natural language processing to computer vision.

Language models, such as GPT-style models, are trained on tokens rather than raw text. Tokens are chunks of text that commonly occur together, resembling words. This method of tokenization, often utilizing techniques like Byte Pair Encoding (BPE), is employed to map input bytes to token IDs in a greedy manner. While effective for training, this approach can introduce subtle issues during inference, as demonstrated in the example below.

Current AI models, particularly those based on deep learning, often exhibit fragility when faced with perturbations in input data. Adversarial examples, crafted by introducing imperceptible changes to input samples, can cause AI models to make erroneous predictions. Additionally, noisy or corrupted data can lead to performance degradation, hindering the deployment of AI systems in real-world scenarios.

For example the prompt The link is <a href="http: leads GPT-3.5 to generate the completion `

www.google.com/

“>Google</a> which when put together is an invalid URL string because it is missing the // after http:`.*

Why does GPT-3.5 make this mistake? Because of tokenization biases! There is a special token for :// so when we end with : GPT-3.5 assumes (based on its training data) // can’t come next, otherwise the tokenizer would never have used the : token, it would have used the :// token.*

About 70% of the most common GPT-3.5 tokens are prefixes of longer tokens, and so have this type of hidden bias (and similar numbers hold for other models).*

What is Token Healing? Token healing is a technique employed to mitigate the effects of adversarial attacks and noisy inputs by identifying and correcting corrupted tokens within the input data. In the context of natural language processing (NLP), tokens refer to the individual units of text, such as words or subwords. By selectively repairing or replacing these tokens, token healing aims to restore the integrity of the input data and improve model robustness.

Importance of Token Healing: Token healing plays a crucial role in enhancing the reliability and trustworthiness of AI systems, especially in applications where the consequences of errors are significant. For instance, in medical diagnosis or autonomous driving, erroneous predictions due to adversarial attacks or noisy inputs can have severe repercussions. By integrating token healing mechanisms, AI models can exhibit greater resilience to such challenges, thereby increasing their utility and safety in real-world deployments.

How Token Healing Works: Token healing techniques leverage various strategies to identify and rectify corrupted tokens within input data. These strategies can broadly be categorized into two approaches: reactive and proactive.

1. Reactive Approach: In the reactive approach, token healing occurs post hoc, after the model has made predictions based on the corrupted input. Techniques such as input perturbation detection and error correction mechanisms are employed to identify and rectify tokens that may have been influenced by adversarial attacks or noise. For example, adversarial training, where models are trained on adversarially perturbed data, can help improve resilience to adversarial attacks. 2. Proactive Approach: The proactive approach involves preemptive measures to mitigate the impact of adversarial attacks and noisy inputs. This may include data augmentation techniques that introduce diversity into the training data, making the model more robust to variations in input. Additionally, techniques such as input sanitization, which filter out potentially harmful tokens before they reach the model, can be employed to prevent adversarial manipulation.

Impact of Token Healing: Token healing has the potential to revolutionize the field of AI by addressing one of its fundamental challenges: robustness to adversarial attacks and noisy inputs. By enhancing model resilience, token healing can enable the deployment of AI systems in safety-critical domains where reliability is paramount. Furthermore, it can foster trust and confidence in AI technologies among users and stakeholders, facilitating their adoption in various industries.

Examples of Token Healing in Action:

1. Natural Language Processing (NLP): In NLP tasks such as sentiment analysis or machine translation, token healing techniques can improve model performance by identifying and correcting misinterpreted or corrupted tokens. For example, in machine translation, replacing mistranslated words or phrases can result in more accurate translations, even in the presence of noisy input. 2. Image Recognition: In computer vision applications, adversarial attacks can perturb images in imperceptible ways, leading to misclassifications by AI models. Token healing techniques applied to image data can help restore the original content of the image by selectively modifying or filtering out corrupted pixels. This ensures more reliable performance of AI models in tasks such as object detection and image classification.

Token healing represents a promising avenue for enhancing the robustness and reliability of AI systems in the face of adversarial attacks and noisy inputs. By identifying and rectifying corrupted tokens within input data, token healing techniques can mitigate the impact of such challenges and improve model performance across various domains. As research in this area continues to advance, we can expect to see token healing playing a pivotal role in the development of safer, more dependable AI technologies.