Tag
This paper investigates whether vision-language models can distinguish potential from established common ground in asymmetric dialogue. Experiments on MapTask data show that providing task-relevant map content (visual or textual) biases models toward over-predicting alignment, as they rely on static referential cues rather than tracking grounding through dialogue history.
This paper extends Frictive Policy Optimization (FPO) to handle perceptual asymmetry in dialogue, where participants hold asymmetric partial information. It demonstrates that evaluating friction from each participant's perspective is more effective than omniscient access, and proposes annotation refinements for grounding states.
This paper investigates a bias in vision-language models where they overestimate shared understanding in dialogue, confusing perceptual access with communicative grounding. The findings have implications for dialogue systems and VLM evaluation.
This paper investigates seemingly contradictory findings on whether large vision-language models (LVLMs) can coordinate efficient referring expressions. The authors show that models can achieve efficiency when explicitly prompted, but fail to infer the need for efficiency from implicit prompts, revealing key differences between human and AI communication.