Pith Number

pith:ADBOX4AN

pith:2023:ADBOX4ANCCJ7NRUBGI7VEQKFUA

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A Survey on Multimodal Large Language Models

Chaoyou Fu, Enhong Chen, Ke Li, Shukang Yin, Sirui Zhao, Tong Xu, Xing Sun

Multimodal large language models use LLMs as a central brain to handle images and other inputs with new emergent reasoning skills.

arxiv:2306.13549 v4 · 2023-06-23 · cs.CV · cs.AI · cs.CL · cs.LG

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The surprising emergent capabilities of MLLM, such as writing stories based on images and OCR-free math reasoning, are rare in traditional multimodal methods, suggesting a potential path to artificial general intelligence.

C2weakest assumption

The survey assumes that the cited literature and the associated GitHub repository together provide a sufficiently complete and up-to-date picture of the rapidly evolving MLLM field.

C3one line summary

This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.

References

209 extracted · 209 resolved · 72 Pith anchors

[1] A Survey of Large Language Models 2023 · arXiv:2303.18223

[2] Chatgpt: A language model for conversational ai, 2023

[3] GPT-4 Technical Report 2023 · arXiv:2303.08774

[4] Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality,

[5] Available: https://vicuna.lmsys.org 1, 3, 4

Formal links

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Cited by

42 papers in Pith

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Large Language Models for Multi-Robot Systems: A Survey

Receipt and verification

First computed	2026-05-17T23:38:49.317953Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

00c2ebf00d1093f6c681323f524145a02d492b2bde1539cd2a569fee780ce57c

Aliases

arxiv: 2306.13549 · arxiv_version: 2306.13549v4 · doi: 10.48550/arxiv.2306.13549 · pith_short_12: ADBOX4ANCCJ7 · pith_short_16: ADBOX4ANCCJ7NRUB · pith_short_8: ADBOX4AN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ADBOX4ANCCJ7NRUBGI7VEQKFUA \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 00c2ebf00d1093f6c681323f524145a02d492b2bde1539cd2a569fee780ce57c

Canonical record JSON

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    "abstract_canon_sha256": "cd8631d64ba42ce8407bc3636a069e8d6555ecab78b44f8bfaf8be644af2f205",
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      "cs.LG"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-06-23T15:21:52Z",
    "title_canon_sha256": "7d7aca4e6ad4070b10cd65d5c70012fc9dbc97638df15425c9356535f8bd8dd4"
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