Object Aligner provides a schema-configurable deterministic similarity metric for JSON objects and graphs using tree alignment and Weisfeiler-Leman referential alignment, applied as a reward in LLM prompt optimization.
FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts
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abstract
The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHabits Shared Task. Our team participated in subtask 1, which aims to detect substance use and abuse mentions in clinical case reports and classify them in four categories (Tobacco, Alcohol, Cannabis, and Drug). We explored various methods of utilizing LLMs for the task, including zero-shot, few-shot, and prompt optimization, and found that GPT-4.1's few-shot prompting performed the best in our experiments. Our method achieved an F1 score of 0.65 on the test set, demonstrating a promising result for recognizing named entities in languages other than English.
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cs.CL 1years
2026 1verdicts
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Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization
Object Aligner provides a schema-configurable deterministic similarity metric for JSON objects and graphs using tree alignment and Weisfeiler-Leman referential alignment, applied as a reward in LLM prompt optimization.