arXiv cs.CL· Jaival Chauhan, Vikram Singh, Pruthwik Mishra·· 11 小时前AI 评分22
基于大语言模型的自动化视频转QA流水线：端到端问题生成与验证
Automated Video-to-QA Pipeline: End-to-End Question Generation and Verification Using Large Language Models
AI 导读
该研究提出一条端到端自动化流水线，从视频中提取结构化摘要并生成高质量问答对：先用 FFmpeg 抽取音频、Whisper 转写，再由 BART 分段摘要，并用 Llama-3.1-8B-Instruct 生成候选 QA 对后回灌模型自验证，仅保留通过验证的高质量样本。
正文 · 原文
Abstract:The exponential growth of educational and informational video content on platforms like YouTube presents a challenge for efficient knowledge retrieval and assessment. This paper proposes an end-to-end automated pipeline for extracting structured summaries and generating high-fidelity Question-Answer (QA) pairs from video streams. The framework integrates a cascade of state-of-the-art models: audio extraction is handled via FFmpeg, followed by automatic speech recognition (ASR) using Whisper (A library in Python) to generate accurate transcripts. Due to the token constraints of Large Language Models (LLMs), transcripts are segmented and summarized using Facebook's BART. A key innovation of this work is a Self-Verification and Fine-Tuning Loop. We utilize meta-llama/Llama-3.1-8BInstruct to generate candidate QA pairs from transcript chunks, which are then fed back into the model to rigorously assess their relevance and importance. Only verified, high-quality pairs are retained to construct a synthetic dataset. This dataset is subsequently used to fine-tune the Llama model using Low-Rank Adaptation (LoRA), resulting in a specialized model capable of generating domain-aligned assessment questions. This approach demonstrates a scalable method for creating self-improving educational tools without the need for manual data annotation. The proposed model can be downloaded from Kaggle with all the codes from GitHub.
| Comments: | 4 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.07755 [cs.CL] |
| (or arXiv:2611.07755v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07755 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pruthwik Mishra [view email]
[v1]
Thu, 5 Nov 2026 10:00:50 UTC (7 KB)
来源：arXiv cs.CL · arxiv.org