57 citations · 422 across the 74 of their papers we have counts for
4 papers · 1 filter
Can It Reach the Generator? Investigating the Survival of Prompt-Injection Attacks in Realistic RAG Settings
Yu Yin, Shuai Wang, Bevan Koopman +1
Recent generative engine optimisation (GEO) research has shown that prompt-injection attacks can push a target product to the top of an LLM's recommendation list, with the stronges…
Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Chunking Strategies for Information Retrieval
Yongjie Zhou, Shuai Wang, Bevan Koopman +1
Document chunking is a critical preprocessing step in dense retrieval systems, yet the design space of chunking strategies remains poorly understood. Recent research has proposed s…
The Vulnerability of LLM Rankers to Prompt Injection Attacks
Yu Yin, Shuai Wang, Bevan Koopman +1
Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within a candidate document (i.e., jailb…
AutoBool: An Reinforcement-Learning trained LLM for Effective Automated Boolean Query Generation for Systematic Reviews
Shuai Wang, Harrisen Scells, Bevan Koopman +1
We present AutoBool, a reinforcement learning (RL) framework that trains large language models (LLMs) to generate effective Boolean queries for medical systematic reviews. Boolean…