Allison Chen

@nociza68 Whoops did I not include it? Clearly a social media newbie... XD arxiv.org/pdf/2510.18039

@allisonchen_227 Super interesting design! Any links to the paper itself?

How should we talk about LLMs? Does it matter if we frame them as a machines 📠, tools ⚒️, or companions 👥? In our #CHI2026 paper, that these framings can alter what people believe about LLMs and how they use them. See 🧵for more!

(6/6) This work was done in collaboration with @sunniesuhyoung, Angel Franyutti, @Amaya_Dharmasir, @kushin_m, @orussakovsky, and @judyefan. I’ll be giving a talk on this work at CHI 2026 at the Modeling Minds and Mentalities Session (Friday April 17 @ 11:15am–Barcelona time)!

(5/6) 📌This matters because it shows that even brief messages about LLMs can shape how people understand, trust, and rely on them. As these systems become embedded in everyday life, the way we talk about them—across research, media, and product design—has real consequences.

New #NVIDIA Paper We introduce Motive, a motion-centric, gradient-based data attribution method that traces which training videos help or hurt video generation. By isolating temporal dynamics from static appearance, Motive identifies which training videos shape motion in video generation. 🔗 research.nvidia.com/labs/sil/proje… 1/10

Today seems to be a fitting day for @GoogleDeepMind news, so I'm excited to announce our new preprint! Prior work suggests that text & img repr's are converging, albeit weakly. We found these same models actually have strong alignment; the inputs were too impoverished to see it!

Prof. Johannes Lutzeyer (@JLutzeyer) and I are co-recruiting a fully funded PhD student at the Department of Computer Science, École Polytechnique in France, to start in September 2026. Details below or at l-yezhu.github.io/assets/PhD_Hir… #PhDhiring #AI

Text-to-image (T2I) models can generate rich supervision for visual learning but generating subtle distinctions still remains challenging. Fine-tuning helps, but too much tuning → overfitting and loss of diversity. How do we preserve fidelity without sacrificing diversity (1/8)

(6/6) This work was done in collaboration with @sunniesuhyoung, @Amaya_Dharmasir, @orussakovsky, and @judyefan. I’ll be presenting this work as a poster at CogSci this Thursday at 1pm (Poster #P1-C-30). Please stop by and say hi! 👋

(5/6) Our work demonstrates the importance of messaging around AI technology in shaping people’s beliefs about them, as people are still navigating how to conceptualize AI. It also highlights the need for appropriate science communication of AI for the general public.

Does how we talk about AI matter? Yes, yes it does! In our #Cogsci2025 paper, we explore how different messages affect what people believe about AI systems. See 🧵for more! Paper: allisonchen.us/assets/portray…

@judyefan @cogsci_soc @Stanford @StanfordPsych i watched this multiple times bc i was so enamored, love it!

Thrilled to welcome members of @cogsci_soc to the SF/Bay area for #CogSci2025 this week! Here's a preview of what the Cognitive Tools Lab 🧠🛠️ @Stanford @StanfordPsych will be presenting!