3 citations · 7 across the 4 of their papers we have counts for
9 papers · 1 filter
Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents
Tianyi Men, Zhuoran Jin, Pengfei Cao +3
As Multimodal Large Language Models (MLLMs) advance, multimodal agents show promise in real-world tasks like web navigation and embodied intelligence. However, due to limitations i…
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment
Zhuoran Jin, Hongbang Yuan, Tianyi Men +4
Despite the significant progress made by existing retrieval augmented language models (RALMs) in providing trustworthy responses and grounding in reliable sources, they often overl…
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation
Jia Fu, Xiaoting Qin, Fangkai Yang +7
Recent advancements in Large Language Models have transformed ML/AI development, necessitating a reevaluation of AutoML principles for the Retrieval-Augmented Generation (RAG) syst…
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models
Hongbang Yuan, Yubo Chen, Pengfei Cao +3
Large language models (LLMs) have achieved remarkable success but still tend to generate factually erroneous responses, a phenomenon known as hallucination. A recent trend is to us…
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
Zhuoran Jin, Pengfei Cao, Chenhao Wang +6
Large language models (LLMs) inevitably memorize sensitive, copyrighted, and harmful knowledge from the training corpus; therefore, it is crucial to erase this knowledge from the m…
Continual Few-shot Event Detection via Hierarchical Augmentation Networks
Chenlong Zhang, Pengfei Cao, Yubo Chen +4
Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce con…