1 citations · 1 across the 5 of their papers we have counts for
6 papers
Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models
Baohang Li, Xiaocheng Feng, Yichong Huang +6
Multi-task post-training of large language models (LLMs) aims to improve performance across tasks with unequal amounts of training data. Existing methods focus primarily on balanci…
IOCC: Aligning Semantic and Cluster Centers for Few-shot Short Text Clustering
Zhihao Yao
In clustering tasks, it is essential to structure the feature space into clear, well-separated distributions. However, because short text representations have limited expressivenes…
Enhancing Non-English Capabilities of English-Centric Large Language Models through Deep Supervision Fine-Tuning
Wenshuai Huo, Xiaocheng Feng, Yichong Huang +9
Large language models (LLMs) have demonstrated significant progress in multilingual language understanding and generation. However, due to the imbalance in training data, their cap…
Ensuring Consistency for In-Image Translation
Chengpeng Fu, Xiaocheng Feng, Yichong Huang +9
The in-image machine translation task involves translating text embedded within images, with the translated results presented in image format. While this task has numerous applicat…
Relay Decoding: Concatenating Large Language Models for Machine Translation
Chengpeng Fu, Xiaocheng Feng, Yichong Huang +5
Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handlin…
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
Yichong Huang, Baohang Li, Xiaocheng Feng +4
Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance. However, this…