collaborators

18 papers

cs.CL2026

What Are We Measuring in NLG? A Meta-Analysis of Evaluation Trends 2020-2025

Jing Yang, Nils Feldhus, Salar Mohtaj +10

As Natural Language Generation (NLG) dominates modern NLP, scalable evaluation remains a critical bottleneck. Consequently, LLM-as-a-judge (LaaJ) adoption has accelerated rapidly,…

cs.CL2026

Judge Circuits

Nils Feldhus, Tanja Baeumel, Elena Golimblevskaia +10

LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its output format changes (e.g.,…

cs.CL2026

Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall

Qianli Wang, Mingyang Wang, Nils Feldhus +5

Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's effects on various LLM capabilit…

cs.CL2026

Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation

Qianli Wang, Van Bach Nguyen, Yihong Liu +6

Counterfactuals refer to minimally edited inputs that cause a model's prediction to change, serving as a promising approach to explaining the model's behavior. Large language model…

cs.CL2026

Simplifying Outcomes of Language Model Component Analyses with ELIA

Aaron Louis Eidt, Nils Feldhus

While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has created an accessibility gap,…

cs.CL2026

Persona Prompting as a Lens on LLM Social Reasoning

Jing Yang, Moritz Hechtbauer, Elisabeth Khalilov +3

For socially sensitive tasks like hate speech detection, the quality of explanations from Large Language Models (LLMs) is crucial for factors like user trust and model alignment. W…