mlsanityData to 7 Oct 2026

MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception

arXiv 2401.07529 ↗First posted 15 Jan 2024cs.CV cs.CLcited by 21

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding. However, these models also suffer from hallucinations, which limit their reliability as AI systems. We believe that these hallucinations are partially due to the models' struggle with understanding what they can and cannot perceive from images, a capability we refer to as self-awareness in perception. Despite its importance, this aspect of MLLMs has been overlooked in prior studies. In this paper, we aim to define and evaluate the self-awareness of MLLMs in perception. To do this, we first introduce the knowledge quadrant in perception, which helps define what MLLMs know and do not know about images. Using this framework, we propose a novel benchmark, the Self-Awareness in Perception for MLLMs (MM-SAP), specifically designed to assess this capability. We apply MM-SAP to a variety of popular MLLMs, offering a comprehensive analysis of their self-awareness and providing detailed insights. The experiment results reveal that current MLLMs possess limited self-awareness capabilities, pointing to a crucial area for future advancement in the development of trustworthy MLLMs. Code and data are available at https://github.com/YHWmz/MM-SAP.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 24 of its 42 references that are in the map. First posted on arXiv 15 Jan 2024.

Builds on

24 of 42 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023ShareGPT4V: Improving Large Multi-Modal Models with Better Captions21 Nov
  2. 2023Improved Baselines with Visual Instruction Tuning5 Oct
  3. 2023MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities4 Aug
  4. 2023MMBench: Is Your Multi-modal Model an All-around Player?12 Jul
  5. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models23 Jun
  6. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning11 May

Other references · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  7. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  8. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  9. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,179
  10. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,582
  11. 2019OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledgecited by 1,114
  12. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 702
6 more
  1. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocationcited by 468
  2. 2023Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuningcited by 439
  3. 2023RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedbackcited by 404
  4. 2021Trustworthy AI: From Principles to Practicescited by 206
  5. 2023Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challengescited by 131
  6. 2023InfMLLM: A Unified Framework for Visual-Language Taskscited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 12; 2025: 7; 2026: 2.

21 papers.

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  1. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 651
  2. 2024A Survey on Benchmarks of Multimodal Large Language Modelscited by 81
  3. 2024A Survey on Evaluation of Multimodal Large Language Modelscited by 46
  4. 2025UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spacescited by 33
  5. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  6. 2024The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?cited by 24
  7. 2025Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)cited by 23
  8. 2025Large language models for artificial general intelligence (AGI): A survey of foundational principles and approachescited by 18
  9. 2024From Imitation to Introspection: Probing Self-Consciousness in Language Modelscited by 14
  10. 2024Right this way: Can VLMs Guide Us to See More to Answer Questions?cited by 10
  11. 2024Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluationscited by 8
  12. 2024Do Large Language Models Possess Sensitive to Sentiment?cited by 3
9 more
  1. 2024Explore the Hallucination on Low-level Perception for MLLMscited by 3
  2. 2025Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Riskscited by 3
  3. 2024Self-controller: Controlling LLMs with Multi-round Step-by-step Self-awarenesscited by 2
  4. 2024Drawing the Line: Enhancing Trustworthiness of MLLMs Through the Power of Refusalcited by 1
  5. 2025Contemplative Artificial Intelligencecited by 1
  6. 2025Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Modelscited by 1
  7. 2025When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMscited by 0
  8. 2026Can Multimodal Large Language Models Truly Understand Small Objects?cited by 0
  9. 2026Uncertainty-Aware Decision Making in Multimodal Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
  2. 2026PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models
  3. 2024Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
  4. 2025HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs
  5. 2024Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning
  6. 2024MVP-Bench: Can Large Vision--Language Models Conduct Multi-level Visual Perception Like Humans?
  7. 2025From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models
  8. 2025Visual Room 2.0: Seeing is Not Understanding for MLLMs