SPoRC-VIST: A Benchmark for Evaluating Generative Natural Narrative in Vision-Language Models

February 09, 2026

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Title: SPoRC-VIST: A Benchmark for Evaluating Generative Natural Narrative in Vision-Language Models
ArXiv ID: 2601.01062
Date: 2026-01-03
Authors: Yunlin Zeng

📝 Abstract

Vision-Language Models (VLMs) have achieved remarkable success in descriptive tasks such as image captioning and visual question answering (VQA). However, their ability to generate engaging, long-form narratives-specifically multi-speaker podcast dialogues-remains under-explored and difficult to evaluate. Standard metrics like BLEU and ROUGE fail to capture the nuances of conversational naturalness, personality, and narrative flow, often rewarding safe, repetitive outputs over engaging storytelling. In this work, we present a novel pipeline for end-to-end visual podcast generation, and fine-tune a Qwen3-VL-32B model on a curated dataset of 4,000 image-dialogue pairs. Crucially, we use a synthetic-to-real training strategy: we train on high-quality podcast dialogues from the Structured Podcast Research Corpus (SPoRC) paired with synthetically generated imagery, and evaluate on real-world photo sequences from the Visual Storytelling Dataset (VIST). This rigorous setup tests the model's ability to generalize from synthetic training data to real-world visual domains. We propose a comprehensive evaluation framework that moves beyond textual overlap, and use AI-as-a-judge (Gemini 3 Pro, Claude Opus 4.5, GPT 5.2) and novel style metrics (average turn len...

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SPoRC-VIST: A Benchmark for Evaluating Generative Natural Narrative in Vision-Language Models

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