6 papers
Metaphor-Induced Algorithmic Steering: Cross-Domain Procedural Transfer in LLM Code Generation
Zhibo Hu, Chen Wang, Yanfeng Shu +3
Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve generalisability across different d…
Metaphors are a Source of Cross-Domain Misalignment of Large Reasoning Models
Zhibo Hu, Chen Wang, Yanfeng Shu +2
Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large language models (LLMs)' reasoning pathways…
Ambiguity in LLMs is a concept missing problem
Zhibo Hu, Chen Wang, Yanfeng Shu +2
Ambiguity in natural language is a significant obstacle for achieving accurate text to structured data mapping through large language models (LLMs), which affects the performance o…
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Shiyi Yang, Zhibo Hu, Xinshu Li +5
Large language model (LLM)-powered agents are increasingly used in recommender systems (RSs) to achieve personalized behavior modeling, where the memory mechanism plays a pivotal r…
Learning Interpretable Scheduling Algorithms for Data Processing Clusters
Zhibo Hu, Chen Wang, Helen +3
Workloads in data processing clusters are often represented in the form of DAG (Directed Acyclic Graph) jobs. Scheduling DAG jobs is challenging. Simple heuristic scheduling algori…
Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models
Zhibo Hu, Chen Wang, Yanfeng Shu +3
The robustness of large language models (LLMs) becomes increasingly important as their use rapidly grows in a wide range of domains. Retrieval-Augmented Generation (RAG) is conside…