20 citations · 24 across the 6 of their papers we have counts for
7 papers
Many Perturbations, Shared Failure Dynamics: Tracing Direction-Sensitive Perturbation Influence in Language Models
Zhibo Hu, Chen Wang, Yanfeng Shu +3
Why can a task-preserving rewrite turn a correct answer into a failure while a larger internal change leaves the answer intact? We develop a direction-sensitive, readout-relative a…
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…
From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance
Qian Fu, Yuzhe Zhang, Yanfeng Shu +3
Antimicrobial-resistant (AMR) microbes are a growing challenge in healthcare, rendering modern medicines ineffective. AMR arises from antibiotic production and bacterial evolution,…
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…