# MM-InstructEval: Zero-Shot Evaluation of (Multimodal) Large Language Models on Multimodal Reasoning Tasks

arXiv: https://arxiv.org/abs/2405.07229 · HTML: https://mlsanity.com/paper/2405.07229/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 12 May 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Built on by (most cited first, 11 of 11)

- [EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models](https://mlsanity.com/paper/2502.04424/) (2025)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models](https://mlsanity.com/paper/2508.09210/) (2025)
- [Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey](https://mlsanity.com/paper/2406.08068/) (2024)
- [Unveiling the Cognitive Compass: Theory-of-Mind-Guided Multimodal Emotion Reasoning](https://mlsanity.com/paper/2602.00971/) (2026)
- [Can Large Vision-Language Models Understand Multimodal Sarcasm?](https://mlsanity.com/paper/2508.03654/) (2025)
- [UAVBench and UAVIT-1M: Benchmarking and Enhancing MLLMs for Low-Altitude UAV Vision-Language Understanding](https://mlsanity.com/paper/2603.14336/) (2026)
- [Can Large Language Models Capture Video Game Engagement?](https://mlsanity.com/paper/2502.04379/) (2025)
- [Social Caption: Evaluating Social Understanding in Multimodal Models](https://mlsanity.com/paper/2601.14569/) (2026)
- [MER-Bench: A Comprehensive Benchmark for Multimodal Meme Reappraisal](https://mlsanity.com/paper/2603.15020/) (2026)
- [Beneath the Scores: Rethinking Hallucination Evaluation for Video Understanding Models](https://mlsanity.com/paper/2609.28991/) (2026)

## Abstract (arXiv)

The emergence of multimodal large language models (MLLMs) has triggered extensive research in model evaluation. While existing evaluation studies primarily focus on unimodal (vision-only) comprehension and reasoning capabilities, they overlook critical assessments of complex multimodal reasoning tasks that require integrated understanding of both visual and textual contexts. Such multimodal tasks present unique challenges, demanding sophisticated reasoning across multiple modalities and deep comprehension of multimodal contexts. In this paper, we present MM-InstructEval, a comprehensive evaluation framework that incorporates diverse metrics to assess model performance across various multimodal reasoning tasks with vision-text contexts. We conduct extensive zero-shot evaluations on 45 models (including 36 MLLMs) across 16 multimodal datasets, encompassing 6 distinct tasks using 10 different instructions. Our framework introduces multiple innovative metrics, including the 'Best Performance' metric to benchmark peak model capabilities, the 'Mean Relative Gain' metric to assess overall efficacy across models and instructions, the 'Stability' metric to measure robustness, and the 'Adaptability' metric to quantify the compatibility between models and instructions. Through comprehensive evaluation and analysis, we uncover several significant insights about model architectures, instruction formats, and their interactions in multimodal reasoning tasks. Our findings establish new benchmarks for assessing the reasoning capabilities of MLLMs and provide strategic guidance for future developments. To facilitate continued research and evaluation in this field, we release our framework and resources at https://github.com/declare-lab/MM-InstructEval, with an interactive leaderboard available at MM-InstructEval Leaderboard (https://declare-lab.github.io/MM-InstructEval/).