A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

Fang Nan; Feng Tian; Jun Liu; Nazaraf Shah; Ping Chen; Qidong Liu; Shilin Sun; Wenbin An

REVIEW 2 major objections 4 minor 4 cited by

A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

T0 review · 2 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This review maps 25 years of multimodal explainable AI onto four eras and three explanation stages, claiming the result is the latest and most complete survey comparison.

desk verdict A useful survey whose four-era historical framing is sloppily applied to the point of undercutting its central claim; worth revising, not dismissing. read the letter →

arxiv 2412.14056 v1 pith:OZ6VG7YB submitted 2024-12-18 cs.CV cs.AIcs.CLcs.LGcs.MM

Shilin Sun , Wenbin An , Feng Tian , Fang Nan , Qidong Liu , Jun Liu , Nazaraf Shah , Ping Chen This is my paper · ORCID

classification cs.CVcs.AIcs.CLcs.LGcs.MM

keywords multimodalexplainableAIXAItaxonomyhistoricalreviewlargelanguagemodelsexplainabilityevaluationmetricsdatasetsfuturechallenges

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review proposes a historical map of Multimodal Explainable AI (MXAI): four eras — traditional machine learning (2000–2009), deep learning (2010–2016), discriminative foundation models (2017–2021), and generative large language models (2022–2024) — each examined through a fixed three-part lens of data, model, and post-hoc explainability. The paper's claim is that this structure, together with its comparison of prior XAI surveys, gives the latest and most complete picture of how MXAI methods evolved and where they stand now. A sympathetic reader would care because a well-founded historical taxonomy turns a scattered literature into a usable map: it lets researchers locate any method in time, see which techniques carry over across eras, and identify what is genuinely new in LLM-based explanation. The review also organizes evaluation metrics and datasets by task and closes by naming four open problems: hallucinations in multimodal LLMs, weak visual understanding, alignment with human cognition, and explainability when no ground truth exists.

What carries the argument

The carrying structure is a two-dimensional taxonomy: four eras defined by milestone models and datasets, crossed with three explanation stages (data, model, post-hoc). The era boundaries are anchored by named milestones — ImageNet in 2009 for the shift from traditional machine learning, the Transformer in 2017 for the shift to discriminative foundation models, and ChatGPT in 2022 for the shift to generative LLMs — while the stage trichotomy supplies a constant analytical grid, so methods from different decades can be compared on the same terms. This grid is what lets the paper present a historical narrative rather than a list of methods.

What would settle it

An audit that runs defined MXAI search queries across standard publication databases for 2000–2024 and finds either a substantial cluster of MXAI methods absent from the review's tables, or a clear case of mis-era assignment (for instance, a Transformer-based explainability method published before 2017 or a generative-LLM explanation method published before 2022), would refute the claimed historical completeness and the 'most comprehensive comparison' label.

Watch

Extended reading notes

Core claim

Stated on the paper's own terms, the central discovery is organizational: the evolution of MXAI can be read as four consecutive eras separated by three technological milestones — ImageNet (2009), Transformer (2017), and ChatGPT (2022) — and within every era the same three explanatory functions recur: explainability of data (before the model), of the model itself (inside the model), and post-hoc explanation of outputs (after the model). Surveying methods from decision trees and SVM rule extraction through CNN visualization and attention-based interpretation to in-context learning, chain-of-thought reasoning, and counterfactual prompting in multimodal LLMs, the paper argues that this three-stage structure persists even as the models and explanation targets change completely. On that basis it claims to be the latest and most comprehensive comparison of XAI reviews, and it uses the taxonomy to organize datasets, evaluation metrics, and future challenges.

Load-bearing premise

The review's historical completeness rests on the assumption that its hand-selected corpus of papers fairly represents the whole MXAI literature, because the scope is described only qualitatively with no search protocol, databases, query strings, or inclusion and exclusion criteria.

Editorial extensions

If this is right

A reader can take any MXAI method from 2000–2024 and locate it in one of twelve cells (four eras by three explanation stages), which makes gaps visible: for example, which stages are thin in which eras.
The review's survey comparison table gives a single baseline for judging what a new XAI survey must cover — unimodal and multimodal, traditional and LLM-based — before claiming novelty.
Because the taxonomy covers LLMs as the current era, it positions explainability techniques such as in-context learning, chain-of-thought, probing, and counterfactual prompting as direct successors of older post-hoc methods, not as a disconnected field.
The assembled datasets and metrics give practitioners a starting checklist for evaluating multimodal explanations by task (VQA, video captioning, recommendations, anomaly understanding).
The four named future challenges — hallucination mitigation, visual weakness in multimodal LLMs, alignment with human cognition, and explanation without ground truth — define a concrete research agenda that follows from the historical gaps.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the era boundary logic holds, a fifth era is already implied: as multimodal models become agentic and embodied, explanation may shift from describing model internals to justifying sequences of actions, which the data/model/post-hoc grid could still absorb but would likely need a new 'world' or 'interaction' category.
The taxonomy's completeness claim could be tested mechanically: an independent search using explicit query strings and inclusion criteria over 2000–2024 literature would reveal whether the hand-selected corpus misses a substantial cluster of MXAI work, and whether any method is assigned to the wrong era.
The three-stage lens treats explanation as a function of where it acts relative to the model; a natural extension is to classify explanations by who consumes them (model developer, domain expert, end user), since the paper's own evaluation discussion relies heavily on human evaluation, which is audience-dependent.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a competent survey of multimodal explainable AI, and the data/model/post-hoc trichotomy within each era is a handy organizing device. Its real value is for newcomers: it puts methods from traditional ML through current LLM-based MXAI in one place, touches on datasets and metrics, and the comparison table of prior surveys is useful. That part deserves credit. The authors also clearly did a lot of literature work; the reference list is broad and the section-level summaries are readable. If you need a starting point for gap-finding, this helps.

The soft spots are real, though, and one of them is load-bearing. The paper's central contribution is the claim that it covers MXAI in four distinct eras (traditional ML, deep learning, discriminative foundation models, generative LLMs) with well-defined boundaries. That claim is internally violated. The deep learning era is dated 2010-2016 (or 2011-2016 in the table header), yet Section IV discusses the Transformer (2017) as an exemplar, along with other 2017 works (influence functions, information theory papers). Conversely, the discriminative foundation models era (2017-2021) contains multiple works from 2023-2024. This is not a typo or a minor inconsistency; it is a systematic mismatch between the stated periodization and the content placed in each era. A reader using the taxonomy as a historical map would be misled about which methods define which period. The fix is straightforward, either move the misplaced citations or, better, reframe the eras as thematic rather than strictly chronological, but as written, the central claim does not hold together.

The other issues are minor: the 'most comprehensive comparison' label is self-assessed with no systematic search protocol; the traditional ML era appears as 2000-2009 in the text and 2000-2010 in a table; and a few metric names are misspelled (BLUE, ROGUE). Those are easy to correct. The absence of a formal contribution is not a problem for a survey, so I do not hold that against it.

Who is this for? Graduate students and researchers wanting a first map of MXAI, and anyone looking for open problems. I would not cite it in this version, because the historical claim is unreliable. But it deserves serious peer review with major revision: the taxonomy is useful, the coverage is broad, and the flaws are fixable. I would send it to reviewers, with explicit instructions to check the era assignments and ask for a systematic scope description.

Referee Report

2 major / 4 minor

Summary. The paper is a narrative review of Multimodal Explainable AI (MXAI). It proposes a four-era historical taxonomy (traditional machine learning 2000-2009, deep learning 2010-2016, discriminative foundation models 2017-2021, and generative large language models 2022-2024), classifies MXAI methods in each era into data, model, and post-hoc explainability, compares the review with prior XAI surveys, and surveys evaluation datasets and metrics. It closes with future challenges such as hallucination mitigation, visual limitations of MLLMs, human-cognition alignment, and evaluation without ground truths.

Significance. If the historical taxonomy were applied consistently, this review would provide a useful map of MXAI evolution and a starting point for identifying open problems. The paper covers a wide range of methods and consolidates them into accessible summary tables, and the associated GitHub repository is a useful resource. However, the central historical claim is currently weakened by internal inconsistencies in era assignment, and the 'most comprehensive comparison' claim rests on an unspecified literature-selection procedure. These issues are fixable but require more than local copyediting.

major comments (2)

[Section IV / Table III / Section II-A] The era boundaries defined in Section II-A are not respected in the era-specific discussion. The deep learning era is defined as 2010-2016, and the next era is defined as beginning with the Transformer in 2017, yet Section IV-B3 presents the Transformer (Vaswani et al., 2017, [1]) as an attention-based network of the deep learning era, and Table III lists 'Attention-based networks [1], [160]-[162]' within that era. The same era also contains multiple other 2017 works, including [158], [159], [162], [167], [170], [172], [173], [177], and [178]. Conversely, Section V (2017-2021) includes works published in 2023-2024, such as [204], [205], [206], [207], and [208]. Because the paper explicitly presents the four eras as chronological periods, these placements undermine the historical-periodization claim and would mislead a reader using the taxonomy as an evolution map. The authors should either reclassify the out-of-era references or explicitly reframe the eras as thematic waves with overlapping time spans.
[Section II-B / Table I] The paper claims to provide 'the latest and most comprehensive comparison of XAI-related reviews,' but it does not describe a systematic search protocol: no databases, query strings, search dates, inclusion/exclusion criteria, or screening procedure are given. Section II-B describes the scope only qualitatively. Without such a protocol, the representativeness of the hand-selected corpus cannot be assessed, and the comprehensiveness claim is unsupported. The authors should either add a reproducible methodology subsection (databases, queries, screening steps) or temper the 'most comprehensive' claim to 'a broad comparison of selected recent surveys.'

minor comments (4)

[Table II / Table III / Section II-A] The era date ranges are inconsistent between the text and table headers: Section II-A and the introduction give the traditional ML era as 2000-2009, but Table II says 2000-2010; the deep learning era is given as 2010-2016 in the text but Table III says 2011-2016. These should be aligned.
[Table VI] Several metric names are misspelled: 'BLUE' should be 'BLEU' in the DD-VQA row, 'ROGUE' should be 'ROUGE' in the Rank2Tell row, 'Aideo' should be 'Audio' in the VAST row, and 'Dinstinct' should be 'Distinct' in the Yan et al. row. Since this table is the paper's consolidated resource for evaluation metrics, these typos impair usability.
[Section II-B] The sentence 'we present the latest and most comprehensive comparison of XAI-related reviews from the past four years XAI-related reviews in the last four years as Table I' contains a duplicated, malformed phrase; it should be rewritten.
[Figure 2] The figure contains label errors: 'Close-sourced' should be 'Closed-source' and 'Mingpt-4' should presumably be 'MiniGPT-4'. These should be corrected for consistency with the rest of the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a narrative survey whose taxonomy is an organizational construct, not a derived prediction.

full rationale

This paper is a literature review rather than a derivation: it categorizes MXAI methods into four historical eras and three explainability types, and it summarizes external methods, datasets, and metrics. No equations are derived, no parameters are fitted, and no quantitative claim is produced from data defined by the claim itself. The era boundaries (2000-2009, 2010-2016, 2017-2021, 2022-2024) are anchored to external milestones such as ImageNet, the Transformer, and ChatGPT, so the taxonomy is not self-definitional in the sense of a fitted parameter renamed as a prediction. The paper does contain self-citations, e.g., references [270], [288], and [294] by the author team, but these are cited as examples of specific methods within the survey (generalized category discovery, attention-based hallucination mitigation, and knowledge acquisition for VQA), not as the justification for the taxonomy or for any load-bearing uniqueness claim. The comparison with prior surveys in Table I is a qualitative positioning statement, not a mathematical reduction. A reader concern about inconsistent era assignment, such as 2017 works appearing in the deep learning era section, would be a correctness or consistency issue, but it is not circularity under the definitions used here: the paper does not derive the taxonomy from itself, and its conclusions do not presuppose the taxonomy's correctness. The survey is self-contained as a narrative review, and the self-citations are minor and non-load-bearing. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey introduces no free parameters or invented entities. Its conclusions rest on the choice of periodization and categorization, which are qualitative modeling assumptions, and on the unstated completeness of the hand-picked literature.

assumptions (3)

domain assumption The four-era division (2000-2009, 2010-2016, 2017-2021, 2022-2024) is a meaningful way to segment MXAI history.
The entire structure of the review depends on this periodization; it is asserted in Section II without external justification or sensitivity analysis.
domain assumption The three explainability categories (data, model, post-hoc) are sufficient and mutually applicable across all eras.
The review classifies every method into these categories (e.g., Tables II-V), but the categories are not justified as exhaustive or validated against other taxonomies.
domain assumption Non-multimodal methods from the traditional ML era (e.g., PCA, decision trees, SVMs) are relevant precursors of MXAI.
Section III frames these unimodal techniques as foundations of multimodal explainability, which is a narrative choice rather than an empirically validated connection.

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Cite this review

Pith. "Pith review of A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future." pith.science (2026). https://pith.science/paper/OZ6VG7YB

@misc{pith2026241214056,
  author       = {Pith},
  title        = {Pith review of: A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OZ6VG7YB}},
  note         = {Machine review of arXiv:2412.14056}
}

read the original abstract

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://github.com/ShilinSun/mxai_review.

Figures

Figures reproduced from arXiv: 2412.14056 by the authors.

**Figure 1.** Illustrative diagram of Multimodal Explainable Artificial Intelli [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** As AI advances, increasing computational power has led to larger [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

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Pith tools

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