11 citations · 18 across the 12 of their papers we have counts for
5 papers · 2 filters
Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)
Francesca Padovani, Bastian Bunzeck, Manar Ali +4
We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we empl…
Identifying the Periodicity of Information in Natural Language
Yulin Ou, Yu Wang, Yang Xu +1
Recent theoretical advancement of information density in natural language has brought the following question on desk: To what degree does natural language exhibit periodicity patte…
Conversational Implicatures: Modelling Relevance Theory Probabilistically
Christoph Unger, Hendrik Buschmeier
Recent advances in Bayesian probability theory and its application to cognitive science in combination with the development of a new generation of computational tools and methods f…
Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models
Yu Wang, Leyi Lao, Langchu Huang +3
Backchannels and fillers are important linguistic expressions in dialogue, but often treated as 'noise' to be bypassed in modern transformer-based language models (LMs). Here, we s…
Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?
Simeon Junker, Manar Ali, Larissa Koch +2
We investigate the linguistic abilities of multimodal large language models in reference resolution tasks featuring simple yet abstract visual stimuli, such as color patches and co…