arXiv subjects

Lisa Aziz-Zadeh

Publications and source records attributed to Lisa Aziz-Zadeh.

3 recordsLinked to original sources

arXiv2026

Anthropomorphism and Trust in Human-Large Language Model interactions

With large language models (LLMs) becoming increasingly prevalent in daily life, so too has the tendency to attribute to them human-like minds and emotions, or anthropomorphize them. Here, we investigate dimensions people use to anthropomorphize and attribute trust toward LLMs across more than 2,000 human-LLM interactions. Participants (N=115) engaged with LLM chatbots systematically varied in warmth (friendliness), competence (capability, coherence), and empathy (cognitive and affective). Warmth and cognitive empathy significantly predicted perceptions on all outcomes (perceived anthropomorphism, trust, similarity, relational closeness, frustration, usefulness), while competence predicted all outcomes except for anthropomorphism. Affective empathy primarily predicted perceived relational measures, but did not predict the epistemic outcomes. Topic sub-analyses showed that more subjective, personally relevant topics (e.g., relationship advice) amplified these effects, producing greater human-likeness and relational connection with the LLM than did objective topics. Together, these findings reveal that warmth, competence, and empathy are key dimensions through which people attribute relational and epistemic perceptions to artificial agents.

cs.HC↗

arXiv2025

Embodiment in multimodal large language models

Multimodal Large Language Models (MLLMs) have demonstrated extraordinary progress in bridging textual and visual inputs. However, MLLMs still face challenges in situated physical and social interactions in sensorally rich, multimodal and real-world settings where the embodied experience of the living organism is essential. We posit that next frontiers for MLLM development require incorporating both internal and external embodiment -- modeling not only external interactions with the world, but also internal states and drives. Here, we describe mechanisms of internal and external embodiment in humans and relate these to current advances in MLLMs in early stages of aligning to human representations. Our dual-embodied framework proposes to model interactions between these forms of embodiment in MLLMs to bridge the gap between multimodal data and world experience.

q-bio.NC↗

arXiv2025

Evaluating point-light biological motion in multimodal large language models

Humans can extract rich semantic information from minimal visual cues, as demonstrated by point-light displays (PLDs), which consist of sparse sets of dots localized to key joints of the human body. This ability emerges early in development and is largely attributed to human embodied experience. Since PLDs isolate body motion as the sole source of meaning, they represent key stimuli for testing the constraints of action understanding in these systems. Here we introduce ActPLD, the first benchmark to evaluate action processing in MLLMs from human PLDs. Tested models include state-of-the-art proprietary and open-source systems on single-actor and socially interacting PLDs. Our results reveal consistently low performance across models, introducing fundamental gaps in action and spatiotemporal understanding.

cs.CV↗