5 papers · 1 filter
MeMo: Memory as a Model
Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong +6
Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications req…
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks
Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu +3
Large Language Models (LLMs), particularly smaller variants, still struggle with complex reasoning tasks. While inference-time prompting can guide reasoning, existing methods often…
REFRAG: Rethinking RAG based Decoding
Xiaoqiang Lin, Aritra Ghosh, Bryan Kian Hsiang Low +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in leveraging extensive external knowledge to enhance responses in multi-turn and agentic applications, such…
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
Zijian Zhou, Xiaoqiang Lin, Xinyi Xu +3
In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without up…
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs
Cheng Wang, Xinyang Lu, See-Kiong Ng +1
The rapid evolution of large language models (LLMs) represents a substantial leap forward in natural language understanding and generation. However, alongside these advancements co…