Meet CMMMU: A New Chinese Massive Multi-Discipline Multimodal Understanding Benchmark Designed to Evaluate Large Multimodal Models LMMs

Within the realm of synthetic intelligence, Giant Multimodal Fashions (LMMs) have exhibited exceptional problem-solving capabilities throughout numerous duties, corresponding to zero-shot picture/video classification, zero-shot picture/video-text retrieval, and multimodal query answering (QA). Nevertheless, latest research spotlight a considerable hole between highly effective LMMs and expert-level synthetic intelligence, notably in duties involving advanced notion and reasoning with domain-specific data. This paper goals to bridge this hole by introducing CMMMU, a pioneering Chinese language benchmark meticulously designed to judge LMMs’ efficiency on an intensive array of multi-discipline duties, guiding the event of bilingual LMMs in direction of reaching expert-level synthetic intelligence.

CMMMU (Chinese language Huge Multi-discipline Multimodal Understanding) stands out as some of the complete benchmarks (some examples are proven in Determine 2), comprising 12,000 manually collected Chinese language multimodal questions sourced from school exams, quizzes, and textbooks. These questions span six core disciplines: Artwork & Design, Enterprise, Science, Well being & Medication, Humanities & Social Science, and Tech & Engineering. Different statistics are proven in Desk 2.  The benchmark not solely evaluates LMMs on advanced reasoning and notion duties but in addition annotates every query with detailed subfields and picture varieties, offering useful insights into the sorts of questions that pose challenges for LMMs.

A 3-stage information assortment course of ensures the richness and variety of CMMMU. Within the first stage, annotator organizers, primarily the authors, accumulate sources adhering to license necessities. Within the second stage, crowdsourcing annotators, consisting of undergraduate college students and people with larger levels, additional annotate the collected sources, strictly following key ideas to filter out unqualified questions with pictures. The third stage includes supplementing inquiries to topics needing extra illustration, making certain a balanced dataset throughout disciplines.

A rigorous information high quality management protocol is applied to boost information high quality additional. At the very least one of many paper’s authors manually verifies every query, filtering out questions with solutions which might be too difficult for LMMs to extract. Moreover, questions not assembly college-level examination requirements are meticulously eliminated. To deal with information contamination considerations, questions that may be appropriately solved by a number of superior LMMs concurrently with out OCR help are filtered out.

The analysis consists of massive language fashions (LLMs) and enormous multimodal fashions (LMMs), contemplating each closed-source and open-source implementations. The zero-shot analysis settings are used as a substitute of fine-tuning or few-shot settings as a result of it supplies a uncooked evaluation of the mannequin’s means to generate correct solutions on multimodal duties. A scientific and rule-based analysis pipeline, incorporating sturdy common expressions and particular guidelines for various query varieties, ensures a complete analysis. Lastly, they’ve adopted micro-average accuracy because the analysis metric. 

As well as, the paper additionally presents a radical error evaluation of 300 samples, showcasing situations the place even top-performing LMMs, corresponding to QwenVL-Plus and GPT-4V, reply incorrectly. The evaluation, distributed amongst 30 topics, highlights challenges main superior LMMs astray and underscores the lengthy journey forward towards reaching expert-level bilingual LMMs. Even essentially the most superior closed-source LMMs, GPT-4V and Qwen-VL-Plus, obtain solely 42% and 36% accuracy, respectively, indicating important room for enchancment.

Curiously, the examine reveals a smaller efficiency hole between open-source and closed-source LMMs in a Chinese language context in comparison with English. Whereas essentially the most highly effective open-source LMM, Qwen-VL-Chat, achieves an accuracy of 28%, with a 14% hole in comparison with GPT-4V, the hole in English is 21%. Notably, Yi-VL-6B1, Yi-VL-34B2, and Qwen-VL-Chat outperform different open-source LMMs on CMMMU, emphasizing their potential within the Chinese language language area. Yi-VL-34B even narrows the efficiency hole between open-source LMMs and GPT-4V on CMMMU to 7%.

In conclusion, the CMMMU benchmark represents a big development within the quest for Superior Normal Intelligence (AGI). It serves as a meticulous evaluator of the newest Giant Multimodal Fashions (LMMs), gauging their elementary perceptual expertise, intricate logical reasoning, and profound domain-specific experience. By evaluating LMMs’ efficiency on CMMMU and MMMU, this analysis supplies insights into the reasoning capability of bilingual LMMs in Chinese language and English contexts, paving the way in which for AGI that rivals seasoned professionals throughout numerous fields.

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