arXiv subjects

Adriana Olmos

Publications and source records attributed to Adriana Olmos.

2 recordsLinked to original sources

arXiv2026

Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player

Video content remains largely inaccessible to blind and low-vision (BLV) users. To address this, we introduce a prototype that leverages a multimodal agent - powered by a novel conversational architecture using a multimodal large language model (MLLM) - to provide BLV users with an interactive, accessible video experience. This Multimodal Agent Video Player (MAVP) demonstrates that an interactive accessibility mode can be added to a video through multilayered prompt orchestration. We describe a user-centered design process involving 18 sessions with BLV users that showed that BLV users do not just want accessibility features, but desire independence and personal agency over the viewing experience. We conducted a qualitative study with an additional 8 BLV participants; in this, we saw that the MAVP's conversational dialogue offers BLV users a sense of personal agency, fostering collaboration and trust. Even in the case of hallucinations, it is meta-conversational dialogues about AI's limitations that can repair trust.

cs.HC↗

arXiv2024

Knowing When to Ask -- Bridging Large Language Models and Data

Large Language Models (LLMs) are prone to generating factually incorrect information when responding to queries that involve numerical and statistical data or other timely facts. In this paper, we present an approach for enhancing the accuracy of LLMs by integrating them with Data Commons, a vast, open-source repository of public statistics from trusted organizations like the United Nations (UN), Center for Disease Control and Prevention (CDC) and global census bureaus. We explore two primary methods: Retrieval Interleaved Generation (RIG), where the LLM is trained to produce natural language queries to retrieve data from Data Commons, and Retrieval Augmented Generation (RAG), where relevant data tables are fetched from Data Commons and used to augment the LLM's prompt. We evaluate these methods on a diverse set of queries, demonstrating their effectiveness in improving the factual accuracy of LLM outputs. Our work represents an early step towards building more trustworthy and reliable LLMs that are grounded in verifiable statistical data and capable of complex factual reasoning.

cs.CL↗