13 papers
Sharpness-Aware Minimization in Logit Space Efficiently Enhances Direct Preference Optimization
Haocheng Luo, Zehang Deng, Thanh-Toan Do +3
Direct Preference Optimization (DPO) has emerged as a popular algorithm for aligning pretrained large language models with human preferences, owing to its simplicity and training s…
Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval
Tuan-Luc Huynh, Weiqing Wang, Trung Le +4
Retrievers are a key bottleneck in Temporal Retrieval-Augmented Generation (RAG) systems: failing to retrieve temporally relevant context can degrade downstream generation, regardl…
Sharpness-Aware Data Generation for Zero-shot Quantization
Dung Hoang-Anh, Cuong Pham Trung Le, Jianfei Cai +1
Zero-shot quantization aims to learn a quantized model from a pre-trained full-precision model with no access to original real training data. The common idea in zero-shot quantizat…
MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora
Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang +5
Continually updating model-based indexes in generative retrieval with new documents remains challenging, as full retraining is computationally expensive and impractical under resou…
PromptDSI: Prompt-based Rehearsal-free Continual Learning for Document Retrieval
Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang +5
Differentiable Search Index (DSI) utilizes pre-trained language models to perform indexing and document retrieval via end-to-end learning without relying on external indexes. Howev…
Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation
Tung-Long Vuong, Hoang Phan, Vy Vo +4
Recent approaches leveraging multi-modal pre-trained models like CLIP for Unsupervised Domain Adaptation (UDA) have shown significant promise in bridging domain gaps and improving…