16 papers
CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction
Linyu Li, Zhi Jin, Yuanpeng He +7
Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aware methods use auxiliary as…
SkillSight: Calibrating Generic Content Bias for Skill Retrieval
Jinying Xiao, Bin Li, Xiaopeng Li +7
As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution. Existi…
TreeProbe : A Tibetan Medicine Benchmark for Cultural Bias in LLMs
Jin Zhang, Linyu Li, Weili Jiang +7
Large language models are increasingly viewed as a potential means of mitigating global health inequities, yet their outputs often reflect dominant high-resource medical traditions…
Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification
Linyu Li, Zhi Jin, Yichi Zhang +6
Enzyme function prediction is a hierarchical, knowledge-intensive form of protein function classification. Existing benchmarks expose an anomaly: general LLMs often get the coarse…
Context-Aware Dynamic Chunking for Streaming Tibetan Speech Recognition
Chao Wang, Yuqing Cai, Renzeng Duojie +3
In this work, we propose a streaming speech recognition framework for Amdo Tibetan, built upon a hybrid CTC/Atten-tion architecture with a context-aware dynamic chunking mechanism.…
POTSA: A Cross-Lingual Speech Alignment Framework for Speech-to-Text Translation
Xuanchen Li, Chenrui Cui, Tianrui Wang +9
Speech Large Language Models have achieved breakthroughs in multilingual speech-to-text translation. However, existing approaches often overlook semantic commonalities across sourc…
Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges
Cheng Huang, Nyima Tashi, Fan Gao +19
Tibetan, one of the major low-resource languages in Asia, presents unique linguistic and sociocultural characteristics that pose both challenges and opportunities for AI research.…
Listening, Imagining & Refining: A Heuristic Optimized ASR Correction Framework with LLMs
Yutong Liu, Ziyue Zhang, Cheng Huang +4
Automatic Speech Recognition (ASR) systems remain prone to errors that affect downstream applications. In this paper, we propose LIR-ASR, a heuristic optimized iterative correction…
TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models
Fan Gao, Cheng Huang, Nyima Tashi +11
To address the severe data scarcity in Tibetan, a low-resource language spoken by over six million people, we introduce TIBSTC-CoT, the large-scale, multi-domain Tibetan dataset au…
RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models
Jin Zhang, Fan Gao, Linyu Li +4
The rise of large language models has led to significant performance breakthroughs in named entity recognition (NER) for high-resource languages, yet there remains substantial room…
TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data Augmentation
Yutong Liu, Feng Xiao, Ziyue Zhang +10
Multi-level Tibetan spelling correction addresses errors at both the character and syllable levels within a unified model. Existing methods focus mainly on single-level correction…
FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation
Yutong Liu, Ziyue Zhang, Ban Ma-bao +7
Tibetan is a low-resource language with minimal parallel speech corpora spanning its three major dialects-Ü-Tsang, Amdo, and Kham-limiting progress in speech modeling. To address t…
TFD: A Comprehensive Structured Tibetan Foundation Dataset for Low-Resource Language Processing and Large-Scale Modeling
Cheng Huang, Fan Gao, Nyima Tashi +5
Large Language Models (LLMs) have achieved remarkable success in high-resource languages, yet progress in Tibetan remains severely constrained. While recent efforts have begun to a…
TLUE: A Tibetan Language Understanding Evaluation Benchmark
Fan Gao, Cheng Huang, Nyima Tashi +9
Large language models have made tremendous progress in recent years, but low-resource languages, like Tibetan, remain significantly underrepresented in their evaluation. Despite Ti…
Progressive Residual Extraction based Pre-training for Speech Representation Learning
Tianrui Wang, Jin Li, Ziyang Ma +8
Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the per…
PEFTT: Parameter-Efficient Fine-Tuning for low-resource Tibetan pre-trained language models
Zhou Mingjun, Daiqing Zhuoma, Qun Nuo +1
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 efficien…