11 citations · 18 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Enhancing Explainability with Multimodal Context Representations for Smarter Robots
Anargh Viswanath, Lokesh Veeramacheneni, Hendrik Buschmeier
Artificial Intelligence (AI) has significantly advanced in recent years, driving innovation across various fields, especially in robotics. Even though robots can perform complex ta…