6 papers
Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs
Jason Chan, Robert Gaizauskas, Zhixue Zhao
As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which to mitigate bias, errors and hal…
Large Language Models Decide Early and Explain Later
Ayan Datta, Zhixue Zhao, Bhuvanesh Verma +3
Large Language Models often achieve strong performance by generating long intermediate chain-of-thought reasoning. However, it remains unclear when a model's final answer is actual…
PERSPECTRA: A Scalable and Configurable Pluralist Benchmark of Perspectives from Arguments
Shangrui Nie, Kian Omoomi, Lucie Flek +2
Pluralism, the capacity to engage with diverse perspectives without collapsing them into a single viewpoint, is critical for developing large language models that faithfully reflec…
Zoom In Disparities in Healthcare LLM Q&A
Ipek Baris Schlicht, Burcu Sayin, Zhixue Zhao +5
Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the reliabili…
From Value Conditioning to Behavioral Shift: Lightweight Value Alignment of LLMs
Shangrui Nie, Florian Mai, David Kaczér +3
Large language models exhibit value-related preferences in their outputs, yet steering such behavior often requires large training datasets or paired preference annotations. In thi…
Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?
Ipek Baris Schlicht, Zhixue Zhao, Burcu Sayin +2
Equitable access to reliable health information is vital for public health, but the quality of online health resources varies by language, raising concerns about inconsistencies in…