5 citations · 7 across the 6 of their papers we have counts for
6 papers
Benchmarking LLMs on the Massive Sound Embedding Benchmark (MSEB)
Cyril Allauzen, Tom Bagby, Georg Heigold +2
The Massive Sound Embedding Benchmark (MSEB) has emerged as a standard for evaluating the functional breadth of audio models. While initial baselines focused on specialized encoder…
Baichuan-M1: Pushing the Medical Capability of Large Language Models
Bingning Wang, Haizhou Zhao, Huozhi Zhou +39
The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like…
NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance
Huan-Yi Su, Ke Wu, Yu-Hao Huang +1
Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, ex…
Long-Form Speech Translation through Segmentation with Finite-State Decoding Constraints on Large Language Models
Arya D. McCarthy, Hao Zhang, Shankar Kumar +2
One challenge in speech translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations. To address this mismatch,…
UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming
Hao Lin, Ke Wu, Jie Li +2
Distributed learning is commonly used for training deep learning models, especially large models. In distributed learning, manual parallelism (MP) methods demand considerable human…
Two Algorithms for Finding Shortest Paths of a Weighted Pushdown Automaton
Ke Wu, Philip Resnik
We introduce efficient algorithms for finding the shortest paths of a weighted pushdown automaton (WPDA), a compact representation of a weighted set of strings with potential a…