# LOVA3: Learning to Visual Question Answering, Asking and Assessment

arXiv: https://arxiv.org/abs/2405.14974 · HTML: https://mlsanity.com/paper/2405.14974/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 86 of its 112 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 30 of 86)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2308.02490/) (2023)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)

## Built on by (most cited first, 21 of 21)

- [Baichuan-Omni-1.5 Technical Report](https://mlsanity.com/paper/2501.15368/) (2025)
- [Don't Just Chase "Highlighted Tokens" in MLLMs: Revisiting Visual Holistic Context Retention](https://mlsanity.com/paper/2510.02912/) (2025)
- [A Survey on Multimodal Benchmarks: In the Era of Large AI Models](https://mlsanity.com/paper/2409.18142/) (2024)
- [UniSVG: A Unified Dataset for Vector Graphic Understanding and Generation with Multimodal Large Language Models](https://mlsanity.com/paper/2508.07766/) (2025)
- [Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs](https://mlsanity.com/paper/2502.16435/) (2025)
- [GraphicBench: A Planning Benchmark for Graphic Design with Language Agents](https://mlsanity.com/paper/2504.11571/) (2025)
- [Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness](https://mlsanity.com/paper/2504.16936/) (2025)
- [Global-Local Dual Perception for MLLMs in High-Resolution Text-Rich Image Translation](https://mlsanity.com/paper/2602.21956/) (2026)
- [UNICBench: UNIfied Counting Benchmark for MLLM](https://mlsanity.com/paper/2603.00595/) (2026)
- [SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models](https://mlsanity.com/paper/2609.01004/) (2026)
- [Spectral Heat Flow for Conservative Token Condensation in Vision-Language Models](https://mlsanity.com/paper/2607.10640/) (2026)
- [InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback](https://mlsanity.com/paper/2502.15027/) (2025)
- [UniAPO: Unified Multimodal Automated Prompt Optimization](https://mlsanity.com/paper/2508.17890/) (2025)
- [Benchmarking Multimodal LLMs on Code Generation for Complex Interactive Webpages](https://mlsanity.com/paper/2606.00154/) (2026)
- [Self-Evolving Visual Questioner](https://mlsanity.com/paper/2606.13929/) (2026)
- [Mitigating Object Hallucination via Robust Local Perception Search](https://mlsanity.com/paper/2506.06729/) (2025)
- [Vision Also You Need: Navigating Out-of-Distribution Detection with Multimodal Large Language Model](https://mlsanity.com/paper/2601.14052/) (2026)
- [Compressing LLMs with MoP: Mixture of Pruners](https://mlsanity.com/paper/2602.06127/) (2026)
- [VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images](https://mlsanity.com/paper/2604.09531/) (2026)
- [Group Preference Collapse in Personalized Multimodal Large Language Models](https://mlsanity.com/paper/2607.22603/) (2026)
- [A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training](https://mlsanity.com/paper/2608.26095/) (2026)

## Abstract (arXiv)

Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3, an innovative framework named "Learning tO Visual question Answering, Asking and Assessment," designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming at fostering the skills of asking and assessing questions in the context of images. To develop the questioning ability, we compile a comprehensive set of multimodal foundational tasks. For assessment, we introduce a new benchmark called EvalQABench, comprising 64,000 training samples (split evenly between positive and negative samples) and 5,000 validation and testing samples. We posit that enhancing MLLMs with the capabilities to answer, ask, and assess questions will enhance their multimodal comprehension, ultimately improving overall performance. To validate this hypothesis, we train MLLMs using the LOVA3 framework and evaluate them on a range of multimodal datasets and benchmarks. Our results demonstrate consistent performance gains, underscoring the critical role of these additional tasks in fostering comprehensive intelligence in MLLMs. The code is available at https://github.com/showlab/LOVA3.