We present a prompt-based evaluation framework for assessing AI-generated math tutoring responses across four pedagogical dimensions: mistake identification, mistake location, guidance quality, and actionability. Our approach leverages task-aware prompt tuning on a large language model, supplemented by data augmentation techniques including dialogue shuffling and class-balanced downsampling. In experiments on the BEA 2025 Shared Task benchmark, our system achieved first place in mistake identification and strong top-five rankings in the other tracks. These results demonstrate the effectiveness of structured prompting and targeted augmentation for enhancing LLMs’ ability to provide pedagogically meaningful feedback.

We propose a multimodal framework integrating textual context and image caption analysis via systematic data augmentation and parameter-efficient fine-tuning. Our approach features: (1) option shuffling to eliminate positional bias, (2) lexical augmentation through synonym replacement and back-translation, and (3) optimized cross-modal ranking adaptation. The system ranks first in Portuguese (Top-1 Acc: 0.92) and second in English (Top-1 Acc: 0.87) on CodaBench. Experiments across 7B-72B models reveal 32B architectures achieve optimal capacity-trainability balance, while larger 72B models suffer from overfitting. Results demonstrate the limitations of GPT-4 knowledge distillation and emphasize controlled data augmentation for idiomatic language learning, advancing multimodal figurative language processing techniques.

Large language models (LLMs) have demonstrated remarkable capabilities in pushing the boundaries of natural language understanding. Nevertheless, the majority of existing open-source LLMs still fall short of meeting satisfactory standards when it comes to addressing numerical problems, especially as the enhancement of their numerical capabilities heavily relies on extensive data.To bridge the gap, we aim to improve the numerical understanding of LLMs by means of efficient data alignment, utilizing only a limited amount of necessary data.Specifically, we first use a data discovery strategy to obtain the most effective portion of numerical data from large datasets. Then, self-augmentation is performed to maximize the potential of the training data. Thirdly, answers of all traning samples are aligned based on some simple rules. Finally, our method achieves the first place in the competition, offering new insights and methodologies for numerical understanding research in LLMs.

This paper explores the potential of social media as a rich source of data for understanding public health trends and behaviors, particularly focusing on emotional well-being and the impact of environmental factors. We employed large language models (LLMs) and developed a suite of knowledge extension techniques to analyze social media content related to mental health issues, specifically examining 1) effects of outdoor spaces on social anxiety symptoms in Reddit,2) tweets reporting children’s medical disorders, and 3) self-reported ages in posts of Twitter and Reddit. Our knowledge extension approach encompasses both supervised data (i.e., sample augmentation and cross-task fine-tuning) and unsupervised data (i.e., knowledge distillation and cross-task pre-training), tackling the inherent challenges of sample imbalance and informality of social media language. The effectiveness of our approach is demonstrated by the superior performance across multiple tasks (i.e., Task 3, 5 and 6) at the SMM4H-2024. Notably, we achieved the best performance in all three tasks, underscoring the utility of our models in real-world applications.