# Beyond Factual QA: Mentorship-Oriented Question Answering over Long-Form Multilingual Content

arXiv: https://arxiv.org/abs/2601.17173 · HTML: https://mlsanity.com/paper/2601.17173/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier](https://mlsanity.com/paper/2412.04261/) (2024)
- [Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages](https://mlsanity.com/paper/2410.16153/) (2024)
- [Towards a Better Metric for Evaluating Question Generation Systems](https://mlsanity.com/paper/1808.10192/) (2018)
- [One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval](https://mlsanity.com/paper/2107.11976/) (2021)
- [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://mlsanity.com/paper/2211.01482/) (2022)
- [QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation](https://mlsanity.com/paper/2406.05707/) (2024)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language](https://mlsanity.com/paper/2406.08519/) (2024)
- [Multi-Agent Interactive Question Generation Framework for Long Document Understanding](https://mlsanity.com/paper/2507.20145/) (2025)
- [E-QGen: Educational Lecture Abstract-based Question Generation System](https://mlsanity.com/paper/2404.13547/) (2024)
- [An End-to-End Workflow using Topic Segmentation and Text Summarisation Methods for Improved Podcast Comprehension](https://mlsanity.com/paper/2307.13394/) (2023)
- [M3FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset](https://mlsanity.com/paper/2506.02510/) (2025)
- [Context Selection and Rewriting for Video-based Educational Question Generation](https://mlsanity.com/paper/2504.19406/) (2025)

## Built on by (most cited first, 1 of 1)

- [MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models](https://mlsanity.com/paper/2609.38543/) (2026)

## Abstract (arXiv)

Question answering systems are typically evaluated on factual correctness, yet many real-world applications-such as education and career guidance-require mentorship: responses that provide reflection and guidance. Existing QA benchmarks rarely capture this distinction, particularly in multilingual and long-form settings. We introduce MentorQA, the first multilingual dataset and evaluation framework for mentorship-focused question answering from long-form videos, comprising nearly 9,000 QA pairs from 180 hours of content across four languages. We define mentorship-focused evaluation dimensions that go beyond factual accuracy, capturing clarity, alignment, and learning value. Using MentorQA, we compare Single-Agent, Dual-Agent, RAG, and Multi-Agent QA architectures under controlled conditions. Multi-Agent pipelines consistently produce higher-quality mentorship responses, with especially strong gains for complex topics and lower-resource languages. We further analyze the reliability of automated LLM-based evaluation, observing substantial variation in alignment with human judgments. Overall, this work establishes mentorship-focused QA as a distinct research problem and provides a multilingual benchmark for studying agentic architectures and evaluation design in educational AI. The dataset and evaluation framework are released at https://github.com/AIM-SCU/MentorQA.