3 citations · 6 across the 25 of their papers we have counts for
4 papers · 1 filter
Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty
Yu Feng, Phu Mon Htut, Zheng Qi +7
Quantifying uncertainty in black-box LLMs is vital for reliable responses and scalable oversight. Existing methods, which gauge a model's uncertainty through evaluating self-consis…
VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
Yang Li, Shaobo Han, Shihao Ji
As the adoption of large language models increases and the need for per-user or per-task model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low-…
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
Xiaokang Zhang, Sijia Luo, Bohan Zhang +12
We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embe…
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
Lu Ye, Ze Tao, Yong Huang +1
Self-attention is an essential component of large language models (LLM) but a significant source of inference latency for long sequences. In multi-tenant LLM serving scenarios, the…