Seven papers accepted to EMNLP 2026, on measuring knowledge diversity and entanglement, cross-cultural probing, and bias removal. What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models. Dustin Wright, Sarah Masud, Jared Moore, Srishti Yadav, Maria Antoniak, Chan Young Park, Isabelle Augenstein. EMNLP main.
Not What, But How: A Communicative Audit of LLM Response Framing. Siddhesh Milind Pawar, Sarah Masud, Haneul Yoo, Alice Oh, Isabelle Augenstein. EMNLP main.
Whose Norms? Disentangling Cultural and Personal Alignment in Large Language Models. Angana Borah, Isabelle Augenstein, Rada Mihalcea. EMNLP main.
Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace Disentanglement. Sekh Mainul Islam, Pepa Atanasova, Isabelle Augenstein. EMNLP Findings.
Bias to Debias: A Simple and Generalizable Framework for Analyzing and Removing Biases through Elicitation. Sekh Mainul Islam, Nadav Borenstein, Siddhesh Milind Pawar, Haeun Yu, Arnav Arora, Isabelle Augenstein. EMNLP Findings.
Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection. Ahmad Dawar Hakimi, Lea Hirlimann, Isabelle Augenstein, Hinrich Schütze. EMNLP Findings.
Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations. Qianli Wang, Nils Feldhus, Pepa Atanasova, Fedor Splitt, Simon Ostermann, Sebastian Möller, Vera Schmitt. EMNLP Findings.