Developing Behavioral Robustness Evaluation Frameworks for LLM-Based Autonomous Agents Against Choice-Architecture Sensitivity and Nudge-Induced Decision Bias

Frameworks that evaluate the behavioural robustness of LLM-based autonomous agents against choice-architecture sensitivity and nudge-induced decision bias.

Frameworks that evaluate the behavioural robustness of LLM-based autonomous agents against choice-architecture sensitivity and nudge-induced decision bias. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are cooperating agents with defined roles and tools, text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses LLM APIs, Python, Hugging Face Transformers, spaCy, and PyTorch, with a clear project structure, setup instructions and sample data so that it can be run and extended.