4 papers
Self-Evolving LLMs via Continual Instruction Tuning
Jiazheng Kang, Le Huang, Cheng Hou +3
In real-world industrial settings, large language models (LLMs) must learn continually to keep pace with diverse and evolving tasks, requiring self-evolution to refine knowledge un…
Invariant debiasing learning for recommendation via biased imputation
Ting Bai, Weijie Chen, Cheng Yang +1
Previous debiasing studies utilize unbiased data to make supervision of model training. They suffer from the high trial risks and experimental costs to obtain unbiased data. Recent…
GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
Ting Bai, Yue Yu, Le Huang +2
The sparse Mixture-of-Experts (MoE) architecture of large language models (LLMs) confronts an inherent issue of load imbalance arising from the simplistic linear router strategy, w…
KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models
Weijie Chen, Ting Bai, Jinbo Su +3
Large language models with retrieval-augmented generation encounter a pivotal challenge in intricate retrieval tasks, e.g., multi-hop question answering, which requires the model t…