Guide to Multimodal Benchmarks
LLMs
AI
A compact field manual for evaluating vision-language models.
Why benchmarks matter
Modern multimodal LLMs are judged by how well they see, read, reason, and chat. Public benchmarks give you:
- a yard-stick (direct comparability),
- a stress-test (spotting failure modes), and
- a fast feedback loop for model or prompt tweaks.
The landscape below is grouped by task family, followed by the metrics and leaderboards you’ll meet most often.
1 Core Task Families & Flagship Datasets
| Task | Representative benchmarks | One-liner |
|---|---|---|
| Visual QA | VQAv2 (Visual Question Answering) · GQA (Computer Science) · OK‑VQA (okvqa.allenai.org) | Open-ended Q&A; GQA stresses compositionality, OK-VQA injects external knowledge. |
| Image Captioning | MS‑COCO (Papers with Code) · NoCaps (nocaps) | Describe images; NoCaps probes novel object generalisation. |
| Diagnostic Reasoning | CLEVR (Computer Science) | Synthetic 3-D scenes that isolate counting, comparison, logic. |
| Real-image Reasoning | NLVR2 (lil.nlp.cornell.edu) · VCR (visualcommonsense.com) | NLVR2 uses image-pairs + T/F statements; VCR adds commonsense inference & rationales. |
| Grounding / Localisation | RefCOCO family (see MS-COCO), Flickr30k-Entities | Map phrases to boxes; evaluate phrase-level grounding. |
| Document & OCR QA | DocVQA (docvqa.org) | Read forms, IDs, invoices; combines OCR and layout understanding. |
| Holistic Diagnostics | MME (arXiv) · MMBench (GitHub) | 14-subtask perception + cognition suite (MME); skill-tagged test bank (MMBench). |
| Exam-style Multidiscipline | MMMU (mmmu-benchmark.github.io) | 11.5 k college-level problems spanning six domains. |
| Multimodal Chat | LLaVA‑Bench (Papers with Code) | Free-form conversations grounded in images, memes, sketches. |
| Math-in-Vision | MathVista (MathVista) | 6 141 diagram-based problems drawn from 28 sources + new IQ sets. |
2 Metrics Cheat-Sheet
| Metric | What it scores | Use cases |
|---|---|---|
| BLEU (Wikipedia) | n-gram precision vs. refs | classic MT & captions |
| CIDEr / CIDEr-D (arXiv) | TF-IDF–weighted consensus | COCO caption leaderboard metric |
| SPICE (arXiv) | scene-graph semantic overlap | counts/attributes in captions |
| CLIPScore (arXiv) | Img–text cosine (no refs) | caption quality without references |
| Exact Match & F1 | string or token overlap | VQA / span QA |
| Accuracy / Soft-VQA | correct answers (soft ≥3/10 agree) | classification, VQA |
| mAP (Medium) | mean AP over IoU slices | detection / grounding |
| IoU (V7 Labs) | box overlap ratio | localisation threshold (≥0.5) |
| Recall@k, MRR, nDCG, Hits@k | ranking relevance | retrieval & KG tasks |
3 Where models rank today
| Leaderboard | Scope | Why check it |
|---|---|---|
| HF Open VLM Leaderboard (Hugging Face) | Open & closed-source VLMs on ≈20 public tests | Quick scorecard for GPT-4o, Gemini Ultra, Claude 3, etc. |
| MME & MMEB leaderboards (arXiv, Hugging Face) | Fine-grained sub-skills | Spot perception vs. cognition gaps. |
Take-aways for practitioners
- Match task to metric. Use CIDEr/SPICE for captions, IoU/AP for grounding, and BLEU/F1 for free-text QA.
- Cross-check robustness. Pass VQAv2? Try OK-VQA or MMU to probe knowledge depth.
- Follow the boards. New models leap-frog weekly; the Open VLM board is the fastest pulse-check.
- Look beyond averages. Skill-tagged suites (MME, MMBench) reveal hidden weaknesses even when headline scores look strong.
- Re-run locally. Most datasets are on Hugging Face; integrate into CI to catch drift after each fine-tune.