11 citations · 18 across the 15 of their papers we have counts for
6 papers · 1 filter
Memory-Augmented Agent Training for Business Document Understanding
Jiale Liu, Yifan Zeng, Malte Højmark-Bertelsen +3
Traditional enterprises face significant challenges in processing business documents, where tasks like extracting transport references from invoices remain largely manual despite t…
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking
Yifan Zeng, Ojas Tendolkar, Raymond Baartmans +3
Ranking passages by prompting a large language model (LLM) can achieve promising performance in modern information retrieval (IR) systems. A common approach to sort the ranking lis…
Assessing and Verifying Task Utility in LLM-Powered Applications
Negar Arabzadeh, Siqing Huo, Nikhil Mehta +5
The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks.…
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search
Zachary Coalson, Huazheng Wang, Qingyun Wu +1
We study the robustness of data-centric methods to find neural network architectures, known as neural architecture search (NAS), against data poisoning. To audit this robustness, w…
Embodied LLM Agents Learn to Cooperate in Organized Teams
Xudong Guo, Kaixuan Huang, Jiale Liu +6
Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-r…
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Yifan Zeng, Yiran Wu, Xiao Zhang +2
Despite extensive pre-training in moral alignment to prevent generating harmful information, large language models (LLMs) remain vulnerable to jailbreak attacks. In this paper, we…