collaborators

5 papers

cs.CL2026

iPOE: Interpretable Prompt Optimization via Explanations

Jiahui Li, Yarik Menchaca Resendiz, Sean Papay +1

Prompt optimization has often been framed as a discrete search problem to find high-performing and robust instructions for an LLM. However, the search result might not make it tran…

cs.CL2025

Are Humans as Brittle as Large Language Models?

Jiahui Li, Sean Papay, Roman Klinger

The output of large language models (LLMs) is unstable, due both to non-determinism of the decoding process as well as to prompt brittleness. While the intrinsic non-determinism of…

cs.LG2025

Regular-pattern-sensitive CRFs for Distant Label Interactions

Sean Papay, Roman Klinger, Sebastian Pado

While LLMs have grown popular in sequence labeling, linear-chain conditional random fields (CRFs) remain a popular alternative with the ability to directly model interactions betwe…

cs.CL2025

Which Demographics do LLMs Default to During Annotation?

Johannes Schäfer, Aidan Combs, Christopher Bagdon +9

Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find it offensive to read a message a…

cs.CL2025

Efficient Language Modeling for Low-Resource Settings with Hybrid RNN-Transformer Architectures

Gabriel Lindenmaier, Sean Papay, Sebastian Padó

Transformer-based language models have recently been at the forefront of active research in text generation. However, these models' advances come at the price of prohibitive traini…