Large Language Models (LLMs) are increasingly being applied in education, showing significant potential in personalized instruction, student feedback, and intelligent tutoring. Generating hints for Math Word Problems (MWPs) has become a critical application, particularly in helping students understand problem-solving steps and logic. However, existing models struggle to provide pedagogically sound guidance that fosters learning without offering direct answers. To address this issue, we introduce TMATH, a dataset specifically designed to evaluate LLMs’ ability to generate high-quality hints for MWPs. TMATH contains diverse mathematical problems paired with carefully crafted, human-generated hints. To assess its impact, we fine-tuned a series of 7B-scale language models using TMATH. Our results, based on quantitative evaluations and expert assessments, show that while LLMs still face challenges in complex reasoning, the TMATH dataset significantly enhances their ability to generate more accurate and contextually appropriate educational hints.

Scenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a scenario. SQA widely exists in the medical, geography, and legal domains—both in practice and in the exams. In this paper, we introduce the GeoSQA dataset. It consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level, where diagrams (e.g., maps, charts) have been manually annotated with natural language descriptions to benefit NLP research. Benchmark results on a variety of state-of-the-art methods for question answering, textual entailment, and reading comprehension demonstrate the unique challenges presented by SQA for future research.