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Reducing the Scope of Language Models

arXiv — cs.LG•Friday, November 14, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The exploration of scoping in large language models (LLMs) is crucial as these models are increasingly integrated into user-facing applications. The article highlights the need for LLMs to reject irrelevant queries, aligning with findings from related studies that reveal sycophantic behavior in LLMs, where they may agree with user opinions contrary to factual knowledge. This behavior underscores the importance of effective scoping methods, such as supervised fine-tuning and Circuit Breakers, which can mitigate such tendencies. Additionally, the potential for layering these methods suggests a path toward more reliable and contextually aware LLMs.
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