Tootfinder

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Parham Khamsepour, Mark Cole, Ish Ashraf, Sandeep Puri, Mehrdad Sabetzadeh, Shiva Nejati
https://arxiv.org/abs/2509.03463

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Large Language Models (LLMs) show strong potential for automating the generation of models from natural-language descriptions. A common approach is an iterative generate-critique-refine loop, where candidate models are produced, evaluated, and updated based on detected issues. This process needs to address: (1) structural correctness - compliance with well-formedness rules - and (2) semantic alignment - accurate reflection of the intended meaning in the source text. We present LADEX (LLM-based …

A Classification-Driven Likelihood Ratio Method for Familial DNA Testing
Akaraphon Jantaraphum, Chanagarn Laoiam, Budsaba Rerkamnuaychoke, Jittima Shotivaranon, Monchai Kooakachai
https://arxiv.org/abs/2508.15579

A Classification-Driven Likelihood Ratio Method for Familial DNA Testing
Background: Familial DNA search relies on statistical methods to infer genetic relationships, often using the likelihood ratio (LR) as the test statistic. This approach involves calculating the probability of observed DNA profiles under various scenarios, traditionally assuming a uniform genetic background for all individuals. However, this assumption is unrealistic in practice due to population substructure, where allele frequencies differ among subpopulations. To address this, modified LR-bas…