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• How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People

  Ricardo E. Gonzalez Penuela, Crescentia Jung, Sharon Lin, Ruiying Hu, Shiri Azenkot · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  This CHI 2026 paper reports a two-week diary study with 20 Blind and Low Vision (BLV) participants (ages 19–75, 11 female/9 male, 13 blind/7 low vision) investigating how multimodal large language models (MLLMs) support real-world access to visual information. The authors built…

  AI · accessibility · multimodal large language models · MLLM · visual question answering

• "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models

  Kapil Garg, Xinru Tang, Jimin Heo, Dwayne R. Morgan, Darren Gergle, Erik B. Sudderth, Anne Marie Piper · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  Garg and colleagues investigate how well Vision-Language Models (VLMs) caption product images taken by blind and low-vision (BLV) people — a high-stakes everyday task that increasingly depends on tools like Be My AI, Microsoft Seeing AI, and general-purpose assistants such as…

  blind and low vision · vision-language models · image captioning · product identification · hallucinations

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