Show HN: Glitchlings, Enemies for Your LLM (github.com)

A new tool titled "Glitchlings" has been introduced to the AI/ML community, offering a novel approach to enhancing the robustness of language models (LLMs) by introducing perturbations to text inputs in a linguistically informed manner. These utilities function as "enemies," applying various forms of corruption—ranging from minor typographical errors to significant semantic shifts—while preserving the intended output. This allows for the creation of challenging scenarios that push LLMs to adapt and generalize better, improving their performance against real-world anomalies. By using Glitchlings, developers can simulate chaotic text conditions and assess how well their models can handle disruptions, thereby identifying weaknesses. The framework is built around customizable Python classes, enabling users to define specific corruption types—such as character-based distortions or homophone substitutions—and apply them systematically to their data. With features like a command-line interface for running experiments and detailed configurations, Glitchlings not only facilitate research but also contribute to more resilient AI systems capable of coping with unexpected variations in input data.