Papers with likelihood ratio test

2 papers
A Likelihood Ratio Test of Genetic Relationship among Languages (2024.naacl-long)

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Challenge: Existing tests of significance for bilateral comparisons are infeasible by design or yield false positives when applied to groups of languages or language families.
Approach: They propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in aligned wordlists.
Outcome: The proposed test solves the problem of false positives on some language families.
Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions (2026.acl-long)

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Challenge: Existing methods for detecting LLM-Generated text suffer from distribution misalignment and limited interpretability.
Approach: They propose a statistical framework utilizing supervised subspace learning to extract compact features and construct conditional semantic distributions based on syntactic structures.
Outcome: The proposed framework is superior in cross-domain, cross-model, and adversarial scenarios.

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