IntroLM: Introspective Language Models via Prefilling-Time Self-Evaluation

ArXi:2601.03511v2 Announce Type: replace-cross A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-quality output for a given query. Existing approaches rely on external classifiers, most commonly BERT based models, which suffer from limited context windows, constrained representational capacity, and additional computational overhead. We propose