Showing cs.CLShow all
3 papers · 1 filter
cs.CL2025
InterpDetect: Interpretable Signals for Detecting Hallucinations in Retrieval-Augmented Generation
Likun Tan, Kuan-Wei Huang, Joy Shi +1
Retrieval-Augmented Generation (RAG) integrates external knowledge to mitigate hallucinations, yet models often generate outputs inconsistent with retrieved content. Accurate hallu…
cs.CL2025
FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
Likun Tan, Kuan-Wei Huang, Kevin Wu
Hallucinations in large language models pose a critical challenge for applications requiring factual reliability, particularly in high-stakes domains such as finance. This work pre…
cs.CL2025
Can Large Language Models Match the Conclusions of Systematic Reviews?
Christopher Polzak, Alejandro Lozano, Min Woo Sun +4
Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based…