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cs.CL2026
Pretrained Persona Mixture Models and Tandem Models for Human Simulation
Minwoo Kang, Téa Wright, Seun Eisape +6
We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas, is inaccurate and produces st…
cs.CL2026
Learning to Refer from Estimated Listener Gaze
Téa Wright, Alane Suhr
We propose to finetune vision-language models to generate more pragmatically optimal referring expressions by transforming observations of incremental listener comprehension, in th…
cs.CL2026
An Investigation of Translationese in the Generations of Multilingual Large Language Models
Maria Valentini, Téa Wright, Julisa Granados +2
Text which has been translated from another language tends to carry with it evidence of translation$\unicode{x2014}$hence, it is often referred to as . Mul…