TLUE: A Tibetan Language Understanding Evaluation Benchmark

Large language models (LLMs) have made tremendous progress in recent years, but low-resource languages, such as Tibetan, remain significantly underrepresented in their evaluation. Despite Tibetan being spoken by over seven million people, it has largely been neglected in the development and assessment of LLMs. To address this gap, we present TLUE (A Tibetan Language Understanding Evaluation Benchmark), the first large-scale benchmark for assessing LLMs' capabilities in Tibetan. TLUE comprises two major components: (1) a comprehensive multi-task understanding benchmark spanning 5 domains and 67 subdomains, and (2) a safety benchmark covering 7 subdomains. We evaluate a diverse set of state-of-the-art LLMs. Experimental results demonstrate that most LLMs perform below the random baseline, highlighting the considerable challenges LLMs face in processing Tibetan, a low-resource language. TLUE provides an essential foundation for driving future research and progress in Tibetan language understanding and underscores the need for greater inclusivity in LLM development.

Progressive Residual Extraction based Pre-training for Speech Representation Learning

Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the performance of pre-trained models on various downstream tasks, each requiring different speech information, poses significant challenges. To this purpose, we propose a progressive residual extraction based self-supervised learning method, named ProgRE. Specifically, we introduce two lightweight and specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to prevent the interference of reinforced pitch variation and speaker information with irrelevant content information learning, we residually remove the information extracted by these two modules from the main branch. The main branch is then trained using HuBERT's speech masking prediction to ensure the performance of the Transformer's deep-layer features on content tasks. In this way, we can progressively extract pitch variation, speaker, and content representations from the input speech. Finally, we can combine multiple representations with diverse speech information using different layer weights to obtain task-specific representations for various downstream tasks. Experimental results indicate that our proposed method achieves joint performance improvements on various tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, compared to excellent SSL methods such as wav2vec2.0, HuBERT, and WavLM.

PEFTT: Parameter-Efficient Fine-Tuning for low-resource Tibetan pre-trained language models

In this era of large language models (LLMs), the traditional training of models has become increasingly unimaginable for regular users and institutions. The exploration of efficient fine-tuning for high-resource languages on these models is an undeniable trend that is gradually gaining popularity. However, there has been very little exploration for various low-resource languages, such as Tibetan. Research in Tibetan NLP is inherently scarce and limited. While there is currently no existing large language model for Tibetan due to its low-resource nature, that day will undoubtedly arrive. Therefore, research on efficient fine-tuning for low-resource language models like Tibetan is highly necessary. Our research can serve as a reference to fill this crucial gap. Efficient fine-tuning strategies for pre-trained language models (PLMs) in Tibetan have seen minimal exploration. We conducted three types of efficient fine-tuning experiments on the publicly available TNCC-title dataset: "prompt-tuning," "Adapter lightweight fine-tuning," and "prompt-tuning + Adapter fine-tuning." The experimental results demonstrate significant improvements using these methods, providing valuable insights for advancing Tibetan language applications in the context of pre-trained models.