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 Collection Timeline

• MIntRec: Released in 2022/10.
• MIntRec2.0: Released in 2024/01.
• MELD: Collected from TV series (released in 2019/05).
• UR-FUNNY-v2: Collected from publicly available TED talks (released in 2019/11).
• MUStARD: Collected from TV series (released in 2019/07).
• MELD-DA: Dialogue act annotations added to MELD in 2020/07. Derived from the EMOTyDA dataset, which re-annotated the original MELD training set videos without collecting new video data.
• IEMOCAP-DA: Dialogue act annotations added to IEMOCAP (released in 2020/07). Derived from the EMOTyDA dataset, which re-annotated all original IEMOCAP videos without collecting new video data.
• MOSI: Collected from YouTube opinion videos (released in 2016/06).
• IEMOCAP: Collected from scripted improvisational acting (released in 2008/12).
• Anno-MI: Collected from publicly available YouTube and Vimeo videos (released in 2023/03).

2.3 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. Data Integrity

All files included in the MMLA benchmark are verified using SHA-256 checksums. Please ensure the integrity of the files using the following checksums:

File Path	SHA256 Hash
/MMLA-Datasets/AnnoMi-client/test.tsv	d555c7131bc54cb61424d421c7a3ec117fa5587c1d4027dd8501321a5d1abc09
/MMLA-Datasets/AnnoMi-client/dev.tsv	4695dc4e1c360cac53ecd0386b82d10fdda9414ad1d559c0a9491a8981657acd
/MMLA-Datasets/AnnoMi-client/train.tsv	8e1104e7d4e42952d0e615c22ee7ea08c03d9b7d07807ba6f4fd4b41d08fed89
/MMLA-Datasets/AnnoMi-client/AnnoMI-client_video.tar.gz	597d9b8c1a701a89c3f6b18e4a451c21b6699a670a83225b7bce5212f5abdfe0
/MMLA-Datasets/AnnoMi-therapist/dev.tsv	bde3ae0e4f16e2249ac94245802b1e5053df3c9d4864f8a889347fe492364767
/MMLA-Datasets/AnnoMi-therapist/test.tsv	0ef6ceeba7dfff9f3201b263aecdb6636b6dd39c5eec220c91a328b5dd23e9d5
/MMLA-Datasets/AnnoMi-therapist/train.tsv	fd0a4741bd3fb32014318f0bd0fbc464a87a9e267163fcac9618707fedca12b2
/MMLA-Datasets/AnnoMi-therapist/AnnoMi-therapist_video.tar.gz	767ce57ad55078001cdd616d642f78d3b0433d9ebcbc14db1608408a54c9fa10
/MMLA-Datasets/CH-SIMSv2.0/test.tsv	40afae5245b1060e8bb5162e8cc4f17f294a43b51a9e01e5bbd64d1f5ebcb6d7
/MMLA-Datasets/CH-SIMSv2.0/dev.tsv	47dfac9ca8d77868ed644b8cd9536fa403f9d6f81e26796cd882e39d2cc14608
/MMLA-Datasets/CH-SIMSv2.0/train.tsv	96350a9e35d62dc63035256e09f033f84aa670f6bf1c06e38daef85d39bde7d7
/MMLA-Datasets/CH-SIMSv2.0/Ch-simsv2_video.tar.gz	e2817c4841a74f9e73eed6cf3196442ff0245f999bdfc5f975dcf18e66348f1e
/MMLA-Datasets/IEMOCAP-DA/dev.tsv	67d357fee50c9b009f9cdc81738e1f45562e0a7f193f6f100320e1881d2b2c8c
/MMLA-Datasets/IEMOCAP-DA/test.tsv	050d27887bec3714f8f0c323594c3c287fa9a5c006f94de0fa09565ba0251773
/MMLA-Datasets/IEMOCAP-DA/train.tsv	823b37fa045aa6aad694d94ad134e23b92491cd6c5d742ed6e9d9456b433608b
/MMLA-Datasets/IEMOCAP/dev.tsv	b6b0bbe1f49dc1f20c4121ac8f943b2d85722c95bb0988946282a496c0c1094d
/MMLA-Datasets/IEMOCAP/test.tsv	7ab10d9c126e037e8c6be1ddf6487d57e9132b2e238286a6a9cccce029760581
/MMLA-Datasets/IEMOCAP/train.tsv	a0017547086721147ed1191e8b7d5da42f795c4070687cffcff001d8827b81d8
/MMLA-Datasets/MELD-DA/test.tsv	b25f4396f30a8d591224ec8074cc4ebfd5727f22fa816ab46cdb455dc22ee854
/MMLA-Datasets/MELD-DA/dev.tsv	4fcc28d139ac933df8e8a288f2d17e010d5e013c70722485a834a7b843536351
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/MMLA-Datasets/MELD-DA/MELD-DA_video.tar.gz	92154bb5d2cf9d8dc229d5fe7ce65519ee7525487f4f42ca7acdf79e48c69707
/MMLA-Datasets/MELD/dev.tsv	ce677f8162ce901e0cc26f531f1786620cac40b7507fa34664537dadc407d256
/MMLA-Datasets/MELD/test.tsv	ee0e0a35a8ae73b522f359039cea34e92d0e13283f5f01c4f29795b439a92a69
/MMLA-Datasets/MELD/train.tsv	063ac8accce2e0da3b45e9cdb077c5374a4cf08f6d62db41438e6e0c52981287
/MMLA-Datasets/MELD/MELD_video.tar.gz	6ce66e5e0d3054aeaf2f5857106360f3b94c37e099bf2e2b17bc1304ef79361b
/MMLA-Datasets/MIntRec/dev.tsv	629ab568ec3e1343c83d76b43d7398f7580361370d09162065a6bb1883f2fe9a
/MMLA-Datasets/MIntRec/test.tsv	adffdc8f061878ad560ee0e0046ba32e6bc9e0332d9e09094cfce0b755fcc2a9
/MMLA-Datasets/MIntRec/train.tsv	c1bec2ff06712063c7399264d7c06f4cdc125084314e6fa8bdfd94d3f0b42332
/MMLA-Datasets/MIntRec/MIntRec_video.tar.gz	a756b6ad5f851773b3ae4621e3aa5c33a662bde80b239a6815a8541c30fc6411
/MMLA-Datasets/MIntRec2.0/dev.tsv	f2f69111d0bd8c26681db0a613a0112f466c667d56a79949ce17ccadd1e6ae37
/MMLA-Datasets/MIntRec2.0/test.tsv	6aa650afbaf40256afdbb546a9f7253511f3fe8d791a9acc7b6829824455a6ed
/MMLA-Datasets/MIntRec2.0/train.tsv	e8b8767bd9a4de5833475db2438e63390c9674041a7b8ea39183a74fa4b624ef
/MMLA-Datasets/MIntRec2.0/MIntRec2.0_video.tar.gz	78bd9ab4a0f9e5768ed2a094524165ecc51926e210a4701a9548d036a68d5e29
/MMLA-Datasets/MOSI/dev.tsv	bd8ccded8dacb9cb7d37743f54c7e4c7bef391069b67b55c7e0cf4626fadee5f
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/MMLA-Datasets/MUStARD/MUStARD_video.tar.gz	8bd863c7ab4c29a710aa3edc0f560361275830a1e98ec41908d51c43e08647c1
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/MMLA-Datasets/UR-FUNNY-v2/dev.tsv	a82f758ef5d2a65bc41e09e24a616d4654c1565e851cd42c71a575b09282a2d2
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/MMLA-Datasets/UR-FUNNY-v2/UR-FUNNYv2_video.tar.gz	e5a3962985c8ead5f593db69ab77a9d6702895768bb5871fe8764406358f8cae

5. 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},
}