2 citations · 3 across the 8 of their papers we have counts for
9 papers
AutoData: Agentic Search for Pre-training Data Selection
Yan Meng, Dhruv Srikanth, Bingchen Zhao +2
LLM agents have recently shown promise in automating machine learning engineering by editing model and training code under execution feedback. Data, however, remains largely outsid…
Dolphin-CN-Dialect: Where Chinese Dialects Matter
Yangyang Meng, Huihang Zhong, Guodong Lin +6
We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolphin-CN-Dialect introduces sub…
Do Language Models Reason Across Languages?
Yan Meng, Wafaa Mohammed, Christof Monz
The real-world information sources are inherently multilingual, which naturally raises a question about whether language models can synthesize information across languages. In this…
Control the Temperature: Selective Sampling for Diverse and High-Quality LLM Outputs
Sergey Troshin, Wafaa Mohammed, Yan Meng +3
Diversity is an essential metric for evaluating the creativity of outputs generated by language models. Temperature-based sampling is a common strategy to increase diversity. Howev…
How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation
Yan Meng, Di Wu, Christof Monz
The massive amounts of web-mined parallel data contain large amounts of noise. Semantic misalignment, as the primary source of the noise, poses a challenge for training machine tra…
How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data
Di Wu, Shaomu Tan, Yan Meng +2
Zero-shot translation aims to translate between language pairs not seen during training in Multilingual Machine Translation (MMT) and is largely considered an open problem. A commo…
Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation
Yan Meng, Christof Monz
Multilingual Machine Translation (MMT) benefits from knowledge transfer across different language pairs. However, improvements in one-to-many translation compared to many-to-one tr…
FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation
Yan Meng, Liangming Pan, Yixin Cao +1
Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question ge…
Parameter-Efficient Fine-Tuning without Introducing New Latency
Baohao Liao, Yan Meng, Christof Monz
Parameter-efficient fine-tuning (PEFT) of pre-trained language models has recently demonstrated remarkable achievements, effectively matching the performance of full fine-tuning wh…