P^3IBench
Harbor: pendingA benchmark with 533 video-query samples evaluating proactive video assistants on perception, risk prediction, and intervention capabilities in real-life streaming scenarios.
- Samples
- 533
- Released
- 2026
- Scores
- 12
P-MMEval
PMMEval — AI evaluation benchmark listed by opencompass.
How to run
ltb · p-mmeval
$ npm i -g lettokenburn-cli$ ltb bench pull p-mmeval
External harness. pip install opencompass && opencompass --models <model_cfg> --datasets flores_gen (OpenCompass dataset config dir 'PMMEval') https://github.com/open-compass/opencompass
License is link-only: ltb bench pull fetches rows from the original host for local evaluation only and does not write a solution/ directory. Do not redistribute the generated tasks.
Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs
Scores
0 sourced entriesNo sourced scores recorded yet.
Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.
Reproduced by the community
All runsLoading community runs…
About
Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. To alleviate this drawback, we aim to present a comprehensive multilingual multitask benchmark. First, we introduce P-MMEval, a large-scale benchmark covering effective fundamental and capability-specialized datasets. Furthermore, P-MMEval delivers consistent language coverage across various datasets and provides parallel samples. Finally, we conduct extensive experiments on representative multilingual model series to compare performances across models and tasks, explore the relationship between multilingual performances and factors such as tasks, model sizes, languages, and prompts, and examine the effectiveness of knowledge transfer from English to other languages. The resulting insights are intended to offer valuable guidance for future research. The dataset is available at https://huggingface.co/datasets/Qwen/P-MMEval.