7 papers
When is a better LLM worth paying for? Stochastic Optimization with Switching Mixed-Fidelity Oracles
Swati Gupta, Tianjiao Li
Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values. Yet LLM-assisted data curation cr…
Index-Translate: A Multilingual Translation Model Family -- Text, Speech, Controlled Dubbing, and Long-Document Translation
Tianjiao Li, Mengran Yu, Chenyu Shi +8
We introduce Index-Translate, a multilingual translation model family that combines a shared multilingual foundation with specialized training for general translation, instruction…
Index SLM Technical Report
Tianjiao Li, Lusheng Zhang, Shien He +11
We present Index-1.9B, a series of open small language models developed at Bilibili. The series comprises four models: Index-1.9B-Base, a foundation model with 1.9 billion non-embe…
HOMURA: Taming the Sand-Glass for Time-Constrained LLM Translation via Reinforcement Learning
Ziang Cui, Mengran Yu, Chenyu Shi +2
Large Language Models (LLMs) have achieved remarkable strides in multilingual translation but are hindered by a systemic cross-lingual verbosity bias, rendering them unsuitable for…
SABER: Switchable and Balanced Training for Efficient LLM Reasoning
Kai Zhao, Yanjun Zhao, Jiaming Song +4
Large language models (LLMs) empowered by chain-of-thought reasoning have achieved impressive accuracy on complex tasks but suffer from excessive inference costs and latency when a…
RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation
Tianjiao Li, Mengran Yu, Chenyu Shi +6
Large language models (LLMs) possess strong multilingual capabilities, and combining Reinforcement Learning from Human Feedback (RLHF) with translation tasks has shown great potent…
LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning
Tao Fang, Derek F. Wong, Lusheng Zhang +5
While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to…