Despite the rapid advancement of LLMs, their performance on linguistically and culturally diverse minority languages within a unified national context remains underexplored.We present CMiLBench, a collection of hierarchical multitask benchmarks designed to translate theoretical notions of diversity in unity (in Chinese: "美美与共") into practical evaluation for three representative Chinese minority languages: Tibetan, Mongolian, and Uyghur.CMiLBench comprises 24,663 instances across 5 difficulty levels and 17 tasks spanning foundational ability, cultural specificity, and safety alignment.We adopt existing dataset adaptation, minority knowledge construction, and high-resource benchmark translation to construct CMiLBench.We assess 14 state-of-the-art commercial and open-source LLMs with a hybrid framework that integrates automatic metrics and LLM-as-a-Judge scoring.The comparative experimental results reveal the gap between theoretical capability and practical utility.CMiLBench serves as a foundational and scalable evaluation resource to bridge the digital language divide and promote the informatization and intelligentization of lowresource Chinese minority languages.

Question generation aims to generate questions according to the given context and answer, and it has made significant progress in both Chinese and English languages. However, research on Tibetan question generation is still in the early stages, with key challenges including the omission of crucial keywords that render questions unanswerable. Existing large-scale models do not provide robust support for low-resource languages, such as GPT or BERT. To solve the problem, this article proposes to generate Tibetan questions based on key sentences and the knowledge graph. The question generator is based on the Transformer model to better understand context and multiple sources of input information. We identify key sentences to leverage closely related information, and construct a knowledge graph to incorporate more distantly related information. The results show that the BLEU-4 reaches 43.92 on TibetanQA, surpassing existing models in Tibetan question generation and significantly improving the answerability of the generated questions.

Instruction Fine-Tuning (IFT) optimizes Large Language Models (LLMs) to enhance the comprehension and execution of user instructions through extensive instruction datasets. However, these datasets are often voluminous, repetitive, and contain a substantial proportion of low-quality data, necessitating effective selection. Traditional data selection methods are plagued by challenges such as insufficient diver-sity, unclear selection criteria, biases from external LLMs, and excessive resource consumption. This paper proposes AlpaCream, a novel instruction data selection methodology designed for industrial applications that aligns with expert insights and ensures maximum diversity of the data. Our approach involves initially categorizing the instruction data into dense clusters using a topic model and then employing a quality assessment model to isolate high-quality instruction data subsets based on their categorizations. The selected data is further improved by using prompt to enhance the data quality. Finally, the augmented data are used to fine-tune the base LLM to get a model with instruction-following solid capability. It can achieve an average performance increase of 31.3% to 39.8% on the Alpaca_52k dataset, which uses 5.76% of the total instruction data. In general, AlpaCream achieved superior performance over comparable instruction data selection methods.