THUIAR/MMLA-Datasets

Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark

1. Introduction

MMLA is the first comprehensive multimodal language analysis benchmark for evaluating foundation models. It has the following features:

• Large Scale: 61K+ multimodal samples.
• Various Sources: 9 datasets.
• Three Modalities: text, video, and audio
• Both Acting and Real-world Scenarios: films, TV series, YouTube, Vimeo, Bilibili, TED, improvised scripts, etc.
• Six Core Dimensions in Multimodal Language Analysis: intent, emotion, sentiment, dialogue act, speaking style, and communication behavior.

We also build baselines with three evaluation methods (i.e., zero-shot inference, supervised fine-tuning, and instruction tuning) on 8 mainstream foundation models (i.e., 5 MLLMs (Qwen2-VL, VideoLLaMA2, LLaVA-Video, LLaVA-OV, MiniCPM-V-2.6), 3 LLMs (InternLM2.5, Qwen2, LLaMA3). More details can refer to our paper.

2. Datasets

2.1 Statistics

Dataset statistics for each dimension in the MMLA benchmark. #C, #U, #Train, #Val, and #Test represent the number of label classes, utterances, training, validation, and testing samples, respectively. avg. and max. refer to the average and maximum lengths.

Dimensions	Datasets	#C	#U	#Train	#Val	#Test	Video Hours	Source	#Video Length (avg. / max.)	#Text Length (avg. / max.)	Language
Intent	MIntRec	20	2,224	1,334	445	445	1.5	TV series	2.4 / 9.6	7.6 / 27.0	English
	MIntRec2.0	30	9,304	6,165	1,106	2,033	7.5	TV series	2.9 / 19.9	8.5 / 46.0
Dialogue Act	MELD	12	9,989	6,992	999	1,998	8.8	TV series	3.2 / 41.1	8.6 / 72.0	English
	IEMOCAP	12	9,416	6,590	942	1,884	11.7	Improvised scripts	4.5 / 34.2	12.4 / 106.0
Emotion	MELD	7	13,708	9,989	1,109	2,610	12.2	TV series	3.2 / 305.0	8.7 / 72.0	English
	IEMOCAP	6	7,532	5,237	521	1,622	9.6	Improvised scripts	4.6 / 34.2	12.8 / 106.0
Sentiment	MOSI	2	2,199	1,284	229	686	2.6	Youtube	4.3 / 52.5	12.5 / 114.0	English
	CH-SIMS v2.0	3	4,403	2,722	647	1,034	4.3	TV series, films	3.6 / 42.7	1.8 / 7.0	Mandarin
Speaking Style	UR-FUNNY-v2	2	9,586	7,612	980	994	12.9	TED	4.8 / 325.7	16.3 / 126.0	English
	MUStARD	2	690	414	138	138	1.0	TV series	5.2 / 20.0	13.1 / 68.0
Communication Behavior	Anno-MI (client)	3	4,713	3,123	461	1,128	10.8	YouTube & Vimeo	8.2 / 600.0	16.3 / 266.0	English
	Anno-MI (therapist)	4	4,773	3,161	472	1,139	12.1		9.1 / 1316.1	17.9 / 205.0

2.2 License

This benchmark uses nine datasets, each of which is employed strictly in accordance with its official license and exclusively for academic research purposes. We fully respect the datasets’ copyright policies, license requirements, and ethical standards. For those datasets whose licenses explicitly permit redistribution, we release the original video data (e.g., MIntRec, MIntRec2.0, MELD, UR-FUNNY-v2, MUStARD, MELD-DA, CH-SIMS v2.0, and Anno-MI. For datasets that restrict video redistribution, users should obtain the videos directly from their official repositories (e.g., MOSI, IEMOCAP and IEMOCAP-DA. In compliance with all relevant licenses, we also provide the original textual data unchanged, together with the specific dataset splits used in our experiments. This approach ensures reproducibility and academic transparency while strictly adhering to copyright obligations and protecting the privacy of individuals featured in the videos.

3. LeaderBoard

3.1 Rank of Zero-shot Inference

RANK	Models	ACC	TYPE
🥇	GPT-4o	52.60	MLLM
🥈	Qwen2-VL-72B	52.55	MLLM
🥉	LLaVA-OV-72B	52.44	MLLM
4	LLaVA-Video-72B	51.64	MLLM
5	InternLM2.5-7B	50.28	LLM
6	Qwen2-7B	48.45	LLM
7	Qwen2-VL-7B	47.12	MLLM
8	Llama3-8B	44.06	LLM
9	LLaVA-Video-7B	43.32	MLLM
10	VideoLLaMA2-7B	42.82	MLLM
11	LLaVA-OV-7B	40.65	MLLM
12	Qwen2-1.5B	40.61	LLM
13	MiniCPM-V-2.6-8B	37.03	MLLM
14	Qwen2-0.5B	22.14	LLM

3.2 Rank of Supervised Fine-tuning (SFT) and Instruction Tuning (IT)

Rank	Models	ACC	Type
🥇	Qwen2-VL-72B (SFT)	69.18	MLLM
🥈	MiniCPM-V-2.6-8B (SFT)	68.88	MLLM
🥉	LLaVA-Video-72B (IT)	68.87	MLLM
4	LLaVA-ov-72B (SFT)	68.67	MLLM
5	Qwen2-VL-72B (IT)	68.64	MLLM
6	LLaVA-Video-72B (SFT)	68.44	MLLM
7	VideoLLaMA2-7B (SFT)	68.30	MLLM
8	Qwen2-VL-7B (SFT)	67.60	MLLM
9	LLaVA-ov-7B (SFT)	67.54	MLLM
10	LLaVA-Video-7B (SFT)	67.47	MLLM
11	Qwen2-VL-7B (IT)	67.34	MLLM
12	MiniCPM-V-2.6-8B (IT)	67.25	MLLM
13	Llama-3-8B (SFT)	66.18	LLM
14	Qwen2-7B (SFT)	66.15	LLM
15	Internlm-2.5-7B (SFT)	65.72	LLM
16	Qwen-2-7B (IT)	64.58	LLM
17	Internlm-2.5-7B (IT)	64.41	LLM
18	Llama-3-8B (IT)	64.16	LLM
19	Qwen2-1.5B (SFT)	64.00	LLM
20	Qwen2-0.5B (SFT)	62.80	LLM

4. Acknowledgements

For more details, please refer to our Github repo. If our work is helpful to your research, please consider citing the following paper:

@article{zhang2025mmla,
  author={Zhang, Hanlei and Li, Zhuohang and Zhu, Yeshuang and Xu, Hua and Wang, Peiwu and Zhu, Haige and Zhou, Jie and Zhang, Jinchao},
  title={Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark},
  year={2025},
  journal={arXiv preprint arXiv:2504.16427},
}