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20242026
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cs.CL2026

A Mechanistic Understanding of Pronoun Fidelity in LLMs

Katharina Trinley, Jesujoba O. Alabi, Dietrich Klakow +1

Faithful and robust pronoun use is important for fair and coherent generations, yet large language models largely fail when multiple referents use different pronouns. To study the…

cs.CL2026

Your Multimodal Speech Model Says I Have a Face for Radio

Maya K. Nachesa, Vlad Niculae, Vagrant Gautam

As large neural models have become better at language tasks, researchers are increasingly building multi- and omnimodal models that handle more modalities of data. One example is t…

cs.CL2026

GRUFF: LLM Pronoun Fidelity, Reasoning, and Biases in German

Fabian Mewes, Anne Lauscher, Vagrant Gautam

Third-person singular pronouns have long been used to study stereotypical biases in language models and to test their abilities to reason about reference. More recently, the interp…

cs.CL2026

Whose Facts Win? LLM Source Preferences under Knowledge Conflicts

Jakob Schuster, Vagrant Gautam, Katja Markert

As large language models (LLMs) are more frequently used in retrieval-augmented generation pipelines, it is increasingly relevant to study their behavior under knowledge conflicts.…

cs.CL2025

Teaching and Critiquing Conceptualization and Operationalization in NLP

Vagrant Gautam

NLP researchers regularly invoke abstract concepts like "interpretability," "bias," "reasoning," and "stereotypes," without defining them. Each subfield has a shared understanding…

cs.CL2025

Aligned Probing: Relating Toxic Behavior and Model Internals

Andreas Waldis, Vagrant Gautam, Anne Lauscher +2

We introduce aligned probing, a novel interpretability framework that aligns the behavior of language models (LMs), based on their outputs, and their internal representations (inte…