Razor of thought: Reflection improves accuracy but can impair recall of operational points for safe operation and detection of hallucinations

Deliberation has become the central paradigm of large-scale linguistic models (LLMS), increasing the accuracy of progress across various benchmarks. However, its suitability for information-sensitive tasks remains unclear. We present the first systematic study of the communication of classification functions under false positive regimes (FPR). Our analysis includes the detection of two tasks and the detection of Hallucinations – tested in well-structured settings and using standard LLMs and high-level reasoning models (LRMS). Our results reveal a clear trade-off: Think about it . In contrast, Think outside the box (No thinking during waking hours) reigns in these critical realms with intelligence, with Think about it passing only when higher FPRs are acceptable. In addition, we get a score based on THEKEN that starts with the artificially sensitive deployment. In the end, the simple combination of these two approaches captures the power of each. Taken together, our findings suggest that it is a double-edged tool: it is beneficial for medium precision, but it is often suitable for applications that require high precision.

• ‡ Equal contribution
• † University of Maryland, College Park
• ** Work Done while at Apple