10 papers
MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models
Zhenpeng Su, Xing Wu, Xue Bai +5
Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonst…
EntropyLong: Effective Long-Context Training via Predictive Uncertainty
Junlong Jia, Ziyang Chen, Xing Wu +5
Training long-context language models to capture long-range dependencies requires specialized data construction. Current approaches, such as generic text concatenation or heuristic…
Libra: Large Chinese-based Safeguard for AI Content
Ziyang Chen, Huimu Yu, Xing Wu +2
Large language models (LLMs) excel in text understanding and generation but raise significant safety and ethical concerns in high-stakes applications. To mitigate these risks, we p…
LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions
Chaochen Gao, Xing Wu, Zijia Lin +2
High-quality long-context instruction data is essential for aligning long-context large language models (LLMs). Despite the public release of models like Qwen and Llama, their long…
CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoning
Huimu Yu, Xing Wu, Haotian Xu +2
Large language models (LLMs) have made significant progress in natural language understanding and generation, driven by scalable pretraining and advanced finetuning. However, enhan…
NExtLong: Toward Effective Long-Context Training without Long Documents
Chaochen Gao, Xing Wu, Zijia Lin +2
Large language models (LLMs) with extended context windows have made significant strides yet remain a challenge due to the scarcity of long documents. Existing methods tend to synt…