FLatS: Principled Out-of-Distribution Detection with Feature-Based
  Likelihood Ratio Score
Haowei Lin Yuntian Gu
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 $\boldsymbol{x}$ through the
likelihood ratio between out-distribution $\mathcal P_{\textit{out}}$ and
in-distribution $\mathcal P_{\textit{in}}$. 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
$p_{\textit{in}}(\boldsymbol{x})$. 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
$p_{\textit{out}}(\boldsymbol{x})$ estimation. Experiments show that FLatS
establishes a new SOTA on popular benchmarks. Our code is publicly available at
https://github.com/linhaowei1/FLatS.