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20242026
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cs.CL2026

Agentic Insight Generation in VSM Simulations

Micha Selak, Dirk Krechel, Adrian Ulges +3

Extracting actionable insights from complex value stream map simulations can be challenging, time-consuming, and error-prone. Recent advances in large language models offer new ave…

cs.CL2025

Measuring Scalar Constructs in Social Science with LLMs

Hauke Licht, Rupak Sarkar, Patrick Y. Wu +4

Many constructs that characterize language, like its complexity or emotionality, have a naturally continuous semantic structure; a public speech is not just "simple" or "complex,"…

cs.CL2025

Controllable Context Sensitivity and the Knob Behind It

Julian Minder, Kevin Du, Niklas Stoehr +4

When making predictions, a language model must trade off how much it relies on its context vs. its prior knowledge. Choosing how sensitive the model is to its context is a fundamen…

cs.CL2025

World Models for Math Story Problems

Andreas Opedal, Niklas Stoehr, Abulhair Saparov +1

Solving math story problems is a complex task for students and NLP models alike, requiring them to understand the world as described in the story and reason over it to compute an a…

cs.CL2024

Activation Scaling for Steering and Interpreting Language Models

Niklas Stoehr, Kevin Du, Vésteinn Snæbjarnarson +3

Given the prompt "Rome is in", can we steer a language model to flip its prediction of an incorrect token "France" to a correct token "Italy" by only multiplying a few relevant act…

cs.CL2024

Context versus Prior Knowledge in Language Models

Kevin Du, Vésteinn Snæbjarnarson, Niklas Stoehr +3

To answer a question, language models often need to integrate prior knowledge learned during pretraining and new information presented in context. We hypothesize that models perfor…