12 papers
Select Smarter, Not More: Prompt-Aware Evaluation Scheduling with Submodular Guarantees
Xiaoyu Ma, Yiwen Li, Haoyue Liu +4
Automatic prompt optimization (APO) hinges on the quality of its evaluation signal, yet scoring every prompt candidate on the full training set is prohibitively expensive. Existing…
Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs
Haoyue Liu, Zhichao Wang, Yongxin Guo +2
Automated prompt optimization is crucial for eliciting reliable reasoning from large language models (LLMs), yet most API-only prompt optimizers iteratively edit monolithic prompts…
PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems
Haozhen Wang, Haoyue Liu, Jionghao Zhu +3
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is often hindered by issues such as o…
Multi-Physics: A Comprehensive Benchmark for Multimodal LLMs Reasoning on Chinese Multi-Subject Physics Problems
Zhongze Luo, Zhenshuai Yin, Yongxin Guo +3
While multimodal LLMs (MLLMs) demonstrate remarkable reasoning progress, their application in specialized scientific domains like physics reveals significant gaps in current evalua…
GRPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance
Yongxin Guo, Wenbo Deng, Zhenglin Cheng +1
Reinforcement Learning with Verifiable Rewards (RLVR) has markedly enhanced the reasoning abilities of large language models (LLMs). Its success, however, largely depends on strong…
Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
Yongxin Guo, Zhenglin Cheng, Xiaoying Tang +2
The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising resu…