Papers with mixture model

4 papers
Agentic Economic Modeling (2026.acl-industry)

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Challenge: AEM is a framework that aligns synthetic LLM choices with small-sample human evidence for reliable econometric inference.
Approach: They introduce a framework that aligns synthetic LLM choices with small-sample human evidence for reliable econometric inference.
Outcome: The proposed framework improves RCT efficiency and establishes a foundation method for LLM-based counterfactual generation.
A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer’s Type (2020.acl-main)

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Challenge: Recent studies show that cognitive manifestations of future dementia may appear as early as 18 years prior to clinical diagnosis . lack of clear diagnosis and prognosis, possibly for an Alzheimer's type, is a major limitation of current methods for identifying dementia-specific cognitive markers.
Approach: They propose to interrogate neural LMs trained on participants with and without dementia by manipulating lexical frequency.
Outcome: The proposed model improves upon the current state-of-the-art for models trained on transcripts of speech produced by healthy participants and those with dementia.
Inducing Semantic Roles Without Syntax (2021.findings-acl)

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Challenge: Semantic roles are a key component of linguistic predicate-argument structure, but syntax can be difficult to define, annotate, and predict.
Approach: They propose to use QA-SRL to automatically induce semantic roles from ontologies that use question-answer pairs to represent predicate-argument structure.
Outcome: The proposed method outperforms existing models and a state-of-the-art model over gold syntax.
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models (2022.acl-long)

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Challenge: Existing methods for SRL identify semantic dependencies that specify the semantic role of arguments in relation to predicates.
Approach: They propose a mixture model-based end-to-end method to model syntactic-semantic dependency correlation in Semantic Role Labeling.
Outcome: The proposed method improves performance in English, German, and Spanish . it achieves small but statistically significant improvement over baseline methods .

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