3 citations · 6 across the 25 of their papers we have counts for
8 papers · 1 filter
BMAM: Brain-inspired Multi-Agent Memory Framework
Yang Li, Jiaxiang Liu, Yusong Wang +2
Language-model-based agents operating over extended interaction horizons face persistent challenges in preserving temporally grounded information and maintaining behavioral consist…
ELPO: Ensemble Learning Based Prompt Optimization for Large Language Models
Qing Zhang, Bing Xu, Xudong Zhang +9
The remarkable performance of Large Language Models (LLMs) highly relies on crafted prompts. However, manual prompt engineering is a laborious process, creating a core bottleneck f…
FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality
Mingda Chen, Yang Li, Xilun Chen +3
Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, le…
UloRL:An Ultra-Long Output Reinforcement Learning Approach for Advancing Large Language Models' Reasoning Abilities
Dong Du, Shulin Liu, Tao Yang +2
Recent advances in large language models (LLMs) have highlighted the potential of reinforcement learning with verifiable rewards (RLVR) to enhance reasoning capabilities through ex…
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
Ling Team, Bin Hu, Cai Chen +43
We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL) to achieve efficient and robust reasoning capabilities. Built…
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…