60 citations · 73 across the 15 of their papers we have counts for
4 papers · 1 filter
Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning
Sara Abdali, Anjali Parikh, Steve Lim +1
Microsoft Windows Feedback Hub is designed to receive customer feedback on a wide variety of subjects including critical topics such as power and battery. Feedback is one of the mo…
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Jingwei Yi, Yueqi Xie, Bin Zhu +4
The integration of large language models with external content has enabled applications such as Microsoft Copilot but also introduced vulnerabilities to indirect prompt injection a…
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia
Giovanni Monea, Maxime Peyrard, Martin Josifoski +6
Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unkno…
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
Javier González, Risa Ueno, Cliff Wong +12
The rapid digitization of real-world data presents an unprecedented opportunity to optimize healthcare delivery and accelerate biomedical discovery. However, these data are often f…