arXiv subjects

Syem Aziz

Publications and source records attributed to Syem Aziz.

3 recordsLinked to original sources

arXiv2026

DATAREEL: Automated Data-Driven Video Story Generation with Animations

Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication. Automatically generating them requires deciding what story to tell, designing effective visualizations, and producing executable animations synchronized with narration. Despite rapid progress in vision-language models (VLMs), it remains unclear how well they can perform this task from a high-level communicative intent, largely because no standardized benchmark exists. We introduce DATAREEL, a benchmark for automated data-driven video story generation containing 328 real-world data reels. Given a data table, a communicative intent, a target duration, and a style reference image, a model must generate executable animation code with synchronized subtitles, which we render and evaluate. Evaluating eight proprietary and open-weight VLMs reveals a substantial capability gap: open-weight models exhibit execution failure rates of up to 39.8%, whereas proprietary models achieve stronger overall performance. Yet even the best-performing models frequently generate static charts, subtitle-animation desynchronization, unstable layouts, and poor adherence to the reference style. We further introduce a strong agentic baseline that decomposes generation into planning, coding, and verification, consistently outperforming direct prompting in both human and automatic evaluations. The task remains far from solved; we release DATAREEL at https://github.com/vis-nlp/DataReel to support future work.

cs.AI↗

arXiv2025

VisionTrap: Unanswerable Questions On Visual Data

Visual Question Answering (VQA) has been a widely studied topic, with extensive research focusing on how VLMs respond to answerable questions based on real-world images. However, there has been limited exploration of how these models handle unanswerable questions, particularly in cases where they should abstain from providing a response. This research investigates VQA performance on unrealistically generated images or asking unanswerable questions, assessing whether models recognize the limitations of their knowledge or attempt to generate incorrect answers. We introduced a dataset, VisionTrap, comprising three categories of unanswerable questions across diverse image types: (1) hybrid entities that fuse objects and animals, (2) objects depicted in unconventional or impossible scenarios, and (3) fictional or non-existent figures. The questions posed are logically structured yet inherently unanswerable, testing whether models can correctly recognize their limitations. Our findings highlight the importance of incorporating such questions into VQA benchmarks to evaluate whether models tend to answer, even when they should abstain.

cs.CV↗

arXiv2024

When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problems

Large language models (LLMs) are increasingly relied upon to solve complex mathematical word problems. However, being susceptible to hallucination, they may generate inaccurate results when presented with unanswerable questions, raising concerns about their potential harm. While GPT models are now widely used and trusted, the exploration of how they can effectively abstain from answering unanswerable math problems and the enhancement of their abstention capabilities has not been rigorously investigated. In this paper, we investigate whether GPTs can appropriately respond to unanswerable math word problems by applying prompts typically used in solvable mathematical scenarios. Our experiments utilize the Unanswerable Word Math Problem (UWMP) dataset, directly leveraging GPT model APIs. Evaluation metrics are introduced, which integrate three key factors: abstention, correctness and confidence. Our findings reveal critical gaps in GPT models and the hallucination it suffers from for unsolvable problems, highlighting the need for improved models capable of better managing uncertainty and complex reasoning in math word problem-solving contexts.

cs.CL↗