4 citations · 5 across the 7 of their papers we have counts for
7 papers
AdvancedMathBench: A Benchmark Suite for Advanced Mathematical Proof Generation and Verification
Lingkai Kong, Zijian Wu, Yuzhe Gu +11
Large language models (LLMs) have achieved remarkable performance on high-school and competition-level mathematics, yet their capabilities on advanced mathematics remain poorly und…
LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling
Wenkai Chen, Tianshu Li, Wenyong Huang +3
Mixture-of-Experts (MoE) and looped architectures scale models along two orthogonal axes, namely parameter capacity and effective depth. However, mainstream looped architectures re…
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Yichun Yin, Wenyong Huang, Kaikai Song +49
We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field…
YODA: Teacher-Student Progressive Learning for Language Models
Jianqiao Lu, Wanjun Zhong, Yufei Wang +10
Although large language models (LLMs) have demonstrated adeptness in a range of tasks, they still lag behind human learning efficiency. This disparity is often linked to the inhere…
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis
Jianqiao Lu, Wenyong Huang, Nianzu Zheng +3
Training a high performance end-to-end speech (E2E) processing model requires an enormous amount of labeled speech data, especially in the era of data-centric artificial intelligen…
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
Kai Chen, Chunwei Wang, Kuo Yang +11
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when…
SELF: Self-Evolution with Language Feedback
Jianqiao Lu, Wanjun Zhong, Wenyong Huang +9
Large Language Models (LLMs) have demonstrated remarkable versatility across various domains. To further advance LLMs, we propose 'SELF' (Self-Evolution with Language Feedback), a…