# Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination

arXiv: https://arxiv.org/abs/2411.12591 · HTML: https://mlsanity.com/paper/2411.12591/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 21 of 21)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021)
- [HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models](https://mlsanity.com/paper/2310.14566/) (2023)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [TempCompass: Do Video LLMs Really Understand Videos?](https://mlsanity.com/paper/2403.00476/) (2024)
- [Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning](https://mlsanity.com/paper/2403.16999/) (2024)
- [Making Large Language Models Better Reasoners with Step-Aware Verifier](https://mlsanity.com/paper/2206.02336/) (2022)
- [Woodpecker: Hallucination Correction for Multimodal Large Language Models](https://mlsanity.com/paper/2310.16045/) (2023)
- [Image-of-Thought Prompting for Visual Reasoning Refinement in Multimodal Large Language Models](https://mlsanity.com/paper/2405.13872/) (2024)
- [CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs](https://mlsanity.com/paper/2401.02582/) (2024)
- [FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2311.01477/) (2023)

## Built on by (most cited first, 29 of 29)

- [Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey](https://mlsanity.com/paper/2503.12605/) (2025)
- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [Unlocking Multimodal Mathematical Reasoning via Process Reward Model](https://mlsanity.com/paper/2501.04686/) (2025)
- [First SFT, Second RL, Third UPT: Continual Improving Multi-Modal LLM Reasoning via Unsupervised Post-Training](https://mlsanity.com/paper/2505.22453/) (2025)
- [SafeMLRM: Demystifying Safety in Multi-modal Large Reasoning Models](https://mlsanity.com/paper/2504.08813/) (2025)
- [A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models](https://mlsanity.com/paper/2509.03871/) (2025)
- [Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)](https://mlsanity.com/paper/2504.03151/) (2025)
- [MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM](https://mlsanity.com/paper/2505.24238/) (2025)
- [Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs](https://mlsanity.com/paper/2510.16062/) (2025)
- [OWL: Geometry-Aware Spatial Reasoning for Audio Large Language Models](https://mlsanity.com/paper/2509.26140/) (2025)
- [Aurelia: Test-time Reasoning Distillation in Audio-Visual LLMs](https://mlsanity.com/paper/2503.23219/) (2025)
- [Mobile-Agent-RAG: Driving Smart Multi-Agent Coordination with Contextual Knowledge Empowerment for Long-Horizon Mobile Automation](https://mlsanity.com/paper/2511.12254/) (2025)
- [WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification](https://mlsanity.com/paper/2509.17740/) (2025)
- [MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment](https://mlsanity.com/paper/2508.17290/) (2025)
- [When to Think and When to Look: Uncertainty-Guided Lookback](https://mlsanity.com/paper/2511.15613/) (2025)
- [Empowering Multimodal LLMs with External Tools: A Comprehensive Survey](https://mlsanity.com/paper/2508.10955/) (2025)
- [DreamPRM-1.5: Unlocking the Potential of Each Instance for Multimodal Process Reward Model Training](https://mlsanity.com/paper/2509.05542/) (2025)
- [From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models](https://mlsanity.com/paper/2511.12861/) (2025)
- [Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2511.19557/) (2025)
- [MMRPT: MultiModal Reinforcement Pre-Training via Masked Vision-Dependent Reasoning](https://mlsanity.com/paper/2512.07203/) (2025)
- [Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models](https://mlsanity.com/paper/2601.22574/) (2026)
- [MM-THEBench: Do Reasoning MLLMs Think Reasonably?](https://mlsanity.com/paper/2601.22735/) (2026)
- [Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning](https://mlsanity.com/paper/2604.27604/) (2026)
- [Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training](https://mlsanity.com/paper/2605.11931/) (2026)
- [CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection](https://mlsanity.com/paper/2607.02930/) (2026)
- [Towards Film-Making Production Dialogue, Narration, Monologue Adaptive Moving Dubbing Benchmarks](https://mlsanity.com/paper/2505.01450/) (2025)
- [Dual Tuning for Reasoning Efficacy-Driven Data Curation in Multimodal LLM Training](https://mlsanity.com/paper/2603.04415/) (2026)
- [A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning](https://mlsanity.com/paper/2603.08291/) (2026)
- [Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2605.11439/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) have advanced the integration of visual and linguistic modalities, establishing themselves as the dominant paradigm for visual-language tasks. Current approaches like chain of thought (CoT) reasoning have augmented the cognitive capabilities of large language models (LLMs), yet their adaptation to MLLMs is hindered by heightened risks of hallucination in cross-modality comprehension. In this paper, we find that the thinking while looking paradigm in current multimodal CoT approaches--where reasoning chains are generated alongside visual input--fails to mitigate hallucinations caused by misleading images. To address these limitations, we propose the Visual Inference Chain (VIC) framework, a novel approach that constructs reasoning chains using textual context alone before introducing visual input, effectively reducing cross-modal biases and enhancing multimodal reasoning accuracy. Comprehensive evaluations demonstrate that VIC significantly improves zero-shot performance across various vision-related tasks, mitigating hallucinations while refining the reasoning capabilities of MLLMs. Our code repository can be found at https://github.com/Terry-Xu-666/visual_inference_chain.