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Robustness of LLMs
Carlo Merola • Jaspinder Singh • Othmane Dardouri
sommario
Large language models (LLMs) have demonstrated impressive emergent abilities across diverse natural language tasks, yet their capacity for structured planning remains under-explored. This project investigates whether the LLaMA model can reliably solve planning problems and introduces a meta-network to evaluate and manage its performance. First, we construct a benchmark suite of planning prompts and record LLaMA’s responses. Next, we train a lightweight meta-network atop these examples to predict, for each prompt, (a) the likelihood that LLaMA’s answer is correct, and (b) a confidence score. When the meta-network predicts low confidence or high error risk, the system automatically delegates the task to alternative solvers or human experts. Our framework provides a generalizable mechanism for risk-aware deployment of LLMs balancing autonomy with reliable fallback strategies.
