most citedDialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

AraMIP: Extending MIPVU Towards Metaphor Identification in Arabic

Mandar Marathe, Manar Ali, Sara Nabhani +3

Metaphor research has gained increasing attention due to its relevance to linguistic creativity, language use, cognitive processes, and related areas. While many efforts have been…

cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests

Manar Ali, Judith Sieker, Sina Zarrieß +1

In human conversation, both interlocutors play an active role in maintaining mutual understanding. When listeners are uncertain about what speakers mean, for example, they can requ…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…