Preemptive Answer

Preemptive answering in large language models (LLMs) and other neural networks refers to the phenomenon where a model generates an answer before completing its full reasoning process, potentially leading to inaccurate conclusions or unreliable performance. Current research focuses on detecting and mitigating this issue, exploring methods like probing the model's internal state during reasoning and incorporating human feedback to improve accuracy and safety, particularly in high-stakes applications. Understanding and addressing preemptive answering is crucial for enhancing the reliability and trustworthiness of LLMs and for optimizing the efficiency of neural network inference in resource-constrained environments like edge computing.