Structured Sentiment Analysis with Large Language Models: A Winning Solution for RuOpinionNE-2024

This paper presents the winning solution for the RuOpinionNE-2024 competition on structured sentiment analysis in Russian news texts.We propose a novel pipeline with large language models (LLMs) and adapter-based fine-tuning, demonstrating how modern LLMs can be effectively adapted to complex opinion tuple extraction tasks.Our method addresses three key challenges: (1) alignment of model predictions with original text spans through a fuzzy substring matching algorithm, (2) robustness to generation variability via multi-prediction aggregation strategies, and (3) efficient domain adaptation using QLoRA fine-tuning.The proposed approach achieved first place with a test F1 score of 0.405.Experimental results reveal that adapter-based fine-tuning of open-source 70B parameter models (Llama-3.3)surpasses prompt-engineered proprietary models like GPT-4o.Our analysis provides practical insights into adapting LLMs for structured information extraction in morphologically rich languages, showing that targeted fine-tuning with 4-bit quantization enables state-of-the-art performance without taskspecific architectures.