5 citations · 6 across the 5 of their papers we have counts for
5 papers
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness
Xiaoning Dong, Chengyan Wu, Yajie Wen +5
Large Language Models (LLMs) can generate factually inaccurate content even if they have corresponding knowledge, which critically undermines their reliability. Existing approaches…
This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA
Hye Sun Yun, Geetika Kapoor, Michael Mackert +4
Patients are increasingly turning to large language models (LLMs) with medical questions that are complex and difficult to articulate clearly. However, LLMs are sensitive to prompt…
Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence
Kaijie Mo, Siddhartha Venkatayogi, Chantal Shaib +4
In high-stakes domains like medicine, it may be generally desirable for models to faithfully adhere to the context provided. But what happens if the context does not align with mod…
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
Sebastian Antony Joseph, Lily Chen, Jan Trienes +5
Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medi…
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Jan Trienes, Sebastian Joseph, Jörg Schlötterer +5
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework…