Detecting out-of-distribution (OOD) instances is crucial for NLP models in practical applications. Although numerous OOD detection methods exist, most of them are empirical. Backed by theoretical analysis, this paper advocates for the measurement of the “OOD-ness” of a test case x through the likelihood ratio between out-distribution \mathcal P_out and in-distribution \mathcal P_in. We argue that the state-of-the-art (SOTA) feature-based OOD detection methods, such as Maha and KNN, are suboptimal since they only estimate in-distribution density p_in(x). To address this issue, we propose FLATS, a principled solution for OOD detection based on likelihood ratio. Moreover, we demonstrate that FLATS can serve as a general framework capable of enhancing other OOD detection methods by incorporating out-distribution density p_out(x) estimation. Experiments show that FLATS establishes a new SOTA on popular benchmarks.