pith. machine review for the scientific record. sign in

arxiv: 2604.16943 · v1 · submitted 2026-04-18 · 💻 cs.CL

Recognition: unknown

MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation

Bo Li , Ningyuan Deng , Tianyu Dong , Shaobo Wang , Shaolin Zhu , Lijie Wen
Authors on Pith no claims yet

Pith reviewed 2026-05-10 07:07 UTC · model grok-4.3

classification 💻 cs.CL
keywords multimodal large language modelsimage translationneuron-aware fine-tuningmodality gapselective parameter updatingactivation analysiscross-modal understanding
0
0 comments X

The pith

Modality neuron-aware fine-tuning selectively updates specialized neurons in multimodal models to close the modality gap for image translation.

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

The paper introduces MNAFT to handle the challenge that multimodal large language models face in capturing fine-grained textual details within images for translation tasks. It does this by first running an instruction-driven analysis to spot which neurons in the vision and language parts are language-agnostic versus language-specific. Only the parameters of the relevant neurons in task-appropriate layers get updated during fine-tuning, leaving the rest of the pre-trained knowledge untouched. If the approach works, models gain better translation accuracy on benchmarks without the redundancy that full fine-tuning creates.

Core claim

MNAFT identifies language-agnostic and language-specific neurons in both vision and language modules through an instruction-driven activation analysis that evaluates their importance across translation tasks. It then performs selective fine-tuning by updating only the parameters of those neurons within selected layers relevant to the target task, while preserving the knowledge encoded in all other neurons and layers. Extensive experiments on multiple benchmarks show this outperforms cascaded models, standard full fine-tuning, and parameter-efficient tuning techniques.

What carries the argument

Instruction-driven activation analysis that locates language-agnostic and language-specific neurons in vision and language modules, followed by selective parameter updates only in those neurons and task-relevant layers.

If this is right

  • Outperforms state-of-the-art cascaded models, full fine-tuning, and parameter-efficient methods on multiple image translation benchmarks.
  • Preserves pre-trained knowledge by leaving most neurons and layers unchanged during adaptation.
  • Yields visualizations of neuron activations and clustering that clarify how different neuron groups support cross-modal understanding.
  • Enables more efficient adaptation of large multimodal models by limiting updates to a small subset of parameters.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same neuron-identification step could be applied to adapt models for other cross-modal tasks such as visual question answering or captioning.
  • If neuron specialization patterns prove consistent across different model sizes, the method might scale to even larger architectures with minimal extra cost.
  • Visualizations of activation patterns could inspire new ways to diagnose and correct modality gaps in existing multimodal systems.

Load-bearing premise

The instruction-driven activation analysis can reliably identify the specialized roles of individual neurons without bias or error.

What would settle it

A controlled experiment that replaces the identified neurons with randomly selected ones for fine-tuning and still matches or exceeds the reported performance gains on the same benchmarks.

read the original abstract

Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual information within images crucial for accurate image translation. This often leads to a modality gap between visual text inputs and textual inputs/outputs for image translation. Existing methods, primarily relying on instruction fine-tuning, risk parameter redundancy of pre-trained knowledge, hindering generalization performance. To address this, we introduce modality neuron-aware fine-tuning (MNAFT), a novel approach that takes advantage of the specialized roles of individual neurons within MLLMs for enhanced image translation. MNAFT identifies language-agnostic and language-specific neurons in both vision and language modules through an instruction-driven activation analysis, evaluating their importance in various translation tasks. We then perform selective fine-tuning, updating only the parameters of language-specific and language-agnostic neurons within the selected layers relevant to the target task, while preserving the knowledge encoded in other neurons and layers. Our extensive experiments on multiple benchmarks demonstrate that MNAFT significantly outperforms state-of-the-art image translation methods, including cascaded models, standard full fine-tuning, and parameter-efficient tuning techniques. Furthermore, we provide comprehensive analysis, including visualizations of neuron activations and clustering patterns, to offer insights into the roles of different neuron groups in mediating cross-modal understanding and facilitating accurate language-specific translation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Modality Neuron-Aware Fine-Tuning (MNAFT) for image translation with MLLMs. It identifies language-agnostic and language-specific neurons in vision and language modules via instruction-driven activation analysis on translation instructions, then selectively updates parameters only for those neurons in chosen layers while preserving knowledge in the remaining neurons and layers. The central claim is that this yields significant outperformance over cascaded models, full fine-tuning, and PEFT baselines on multiple benchmarks, with supporting visualizations of activations and clustering.

Significance. If the empirical results and neuron-identification procedure are shown to be robust, MNAFT could advance parameter-efficient adaptation of MLLMs by reducing redundancy while targeting modality gaps in cross-modal tasks. The provided visualizations of neuron activations and clustering patterns constitute a positive contribution for interpretability.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments: The claim that MNAFT 'significantly outperforms' SOTA methods is presented without any quantitative metrics, baselines, dataset sizes, or error bars in the abstract and is not accompanied by the required ablation controls (random neuron selection, gradient-based selection, or consistency across random seeds) needed to isolate the contribution of the instruction-driven activation analysis.
  2. [Method] Method description: The procedure for classifying neurons as language-agnostic versus language-specific relies on activation analysis but supplies no explicit threshold, statistical test, or bias-control experiment (e.g., comparison to task-difficulty proxies), which is load-bearing for the claim that selective updates preserve pre-trained cross-modal knowledge.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including at least one key quantitative result (e.g., BLEU or accuracy delta) to support the outperformance statement.
  2. [Method] Notation for neuron groups (language-agnostic/specific) should be defined once with a clear equation or pseudocode block for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below and will revise the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments: The claim that MNAFT 'significantly outperforms' SOTA methods is presented without any quantitative metrics, baselines, dataset sizes, or error bars in the abstract and is not accompanied by the required ablation controls (random neuron selection, gradient-based selection, or consistency across random seeds) needed to isolate the contribution of the instruction-driven activation analysis.

    Authors: We agree that the abstract would be strengthened by including concrete quantitative results. In the revision we will add specific performance gains (e.g., average BLEU or accuracy improvements over baselines), dataset sizes, and reference to the main baselines. To isolate the contribution of our neuron-selection procedure we will add the requested ablations: (1) random neuron selection within the same layers, (2) gradient-based neuron importance, and (3) results across multiple random seeds reported with error bars. These new controls will appear in the experiments section and will be summarized in the abstract. revision: yes

  2. Referee: [Method] Method description: The procedure for classifying neurons as language-agnostic versus language-specific relies on activation analysis but supplies no explicit threshold, statistical test, or bias-control experiment (e.g., comparison to task-difficulty proxies), which is load-bearing for the claim that selective updates preserve pre-trained cross-modal knowledge.

    Authors: We acknowledge that the current description of the neuron-classification step lacks sufficient detail. In the revised method section we will (a) state the exact threshold rule used (e.g., top-k percentile of activation-difference scores), (b) report a statistical test (e.g., paired t-test or Wilcoxon test) on the activation differences between translation and non-translation instructions, and (c) add a control experiment that compares our selection against simple task-difficulty proxies (e.g., sentence length or perplexity) to demonstrate that the identified neurons are modality-specific rather than merely difficulty-driven. These additions will directly support the claim that selective updates preserve pre-trained cross-modal knowledge. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical method with independent experimental validation

full rationale

The paper introduces MNAFT as a selective fine-tuning procedure that first performs instruction-driven activation analysis to label neurons as language-agnostic or language-specific, then updates only selected parameters while preserving others. This procedure is presented as an empirical technique whose effectiveness is measured by direct comparison against full fine-tuning, PEFT baselines, and cascaded models on standard benchmarks. No equations, parameter fits, or uniqueness claims are shown to reduce by construction to the inputs; the activation analysis is a data-driven step whose outputs are not presupposed by the performance claims. No self-citation is invoked as a load-bearing uniqueness theorem, and no ansatz or renaming of known results is used to substitute for derivation. The derivation chain therefore remains self-contained and externally falsifiable via the reported experiments.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unverified premise that neurons possess identifiable specialized roles that can be isolated without side effects on other capabilities.

axioms (1)
  • domain assumption Individual neurons within vision and language modules of MLLMs have distinct language-agnostic and language-specific roles that can be identified through instruction-driven activation analysis.
    This categorization underpins the decision of which parameters to update versus preserve.

pith-pipeline@v0.9.0 · 5553 in / 1190 out tokens · 45725 ms · 2026-05-10T07:07:29.092289+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

20 extracted references · 11 canonical work pages · 4 internal anchors

  1. [1]

    Instructblip: Towards general-purpose vision-language models with instruction tuning

    1 Dai W, Li J, Li D, et al. Instructblip: Towards general-purpose vision-language models with instruction tuning. Advances in neural information processing systems, 2023, 36: 49250–49267 2 Li J, Li D, Savarese S, et al. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In: Proceedings of International ...

    2023

  2. [2]

    Multimodal large language models: A survey

    19730–19742 3 Wu J, Gan W, Chen Z, et al. Multimodal large language models: A survey. In: Proceedings of 2023 IEEE International Conference on Big Data (BigData),

    2023

  3. [3]

    arXiv preprint arXiv:2401.13601(2024)

    2247–2256 4 Zhang D, Yu Y, Dong J, et al. Mm-llms: Recent advances in multimodal large language models. arXiv preprint arXiv:2401.13601, 2024 5 Gemini T. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024 6 Hong W, Wang W, Ding M, et al. Cogvlm2: Visual language models for image and video understanding. arXiv preprin...

    work page arXiv 2024

  4. [4]

    Exploring better text image translation with multimodal codebook

    13433–13447 9 Lan Z, Yu J, Li X, et al. Exploring better text image translation with multimodal codebook. arXiv preprint arXiv:2305.17415, 2023 10 Watanabe Y, Okada Y, Kim Y B, et al. Translation camera. In: Proceedings of Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No. 98EX170),

    work page arXiv 2023

  5. [5]

    Automatic detection and translation of text from natural scenes

    613–617 11 Yang J, Chen X, Zhang J, et al. Automatic detection and translation of text from natural scenes. In: Proceedings of 2002 IEEE International conference on acoustics, speech, and signal processing,

    2002

  6. [6]

    Translatotron-V(ison): An end-to-end model for in-image machine translation

    109–116 13 Lan Z, Niu L, Meng F, et al. Translatotron-V(ison): An end-to-end model for in-image machine translation. In: Proceedings of Ku L W, Martins A, Srikumar V, editors, Findings of the Association for Computational Linguistics ACL 2024,

    2024

  7. [7]

    Towards end-to-end in-image neural machine translation

    5472–5485 14 Mansimov E, Stern M, Chen M, et al. Towards end-to-end in-image neural machine translation. arXiv preprint arXiv:2010.10648, 2020 15 Li B, Zhu S, Wen L. MIT-10M: A large scale parallel corpus of multilingual image translation. In: Proceedings of Rambow O, Wanner L, Apidianaki M, et al., editors, Proceedings of the 31st International Conferenc...

    work page arXiv 2010

  8. [8]

    Image translation network

    5154–5167 16 Jain P, Firat O, Ge Q, et al. Image translation network. Github.com, 2021 17 Ma C, Zhang Y, Tu M, et al. Improving end-to-end text image translation from the auxiliary text translation task. 2022 26th International Conference on Pattern Recognition (ICPR), 2022, pages 1664–1670 18 Ma C, Zhang Y, Tu M, et al. Multi-teacher knowledge distillati...

    2021

  9. [9]

    Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

    484–501 19 Bai J, Bai S, Yang S, et al. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023 20 Chen Z, Wu J, Wang W, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In: Proceedings of Proceedings of the IEEE/CVF Conference on Computer Vision ...

    work page internal anchor Pith review arXiv 2023

  10. [10]

    MiniCPM-V: A GPT-4V Level MLLM on Your Phone

    24185–24198 21 Liu H, Li C, Li Y, et al. Llava-next: Improved reasoning, ocr, and world knowledge, January 2024 22 Yao Y, Yu T, Zhang A, et al. Minicpm-v: A gpt-4v level mllm on your phone. arXiv preprint arXiv:2408.01800, 2024 23 Lu H, Liu W, Zhang B, et al. Deepseek-vl: towards real-world vision-language understanding. arXiv preprint arXiv:2403.05525, 2...

    work page internal anchor Pith review arXiv 2024

  11. [11]

    Accessed: 2026-03-23 25 Bai S, Chen K, Liu X, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923, 2025 26 Tang T, Luo W, Huang H, et al. Language-specific neurons: The key to multilingual capabilities in large language models. arXiv preprint arXiv:2402.16438, 2024 27 Goodfellow I J, Bulatov Y, Ibarz J, et al. Multi-digit number recogniti...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  12. [12]

    Finetuned Language Models Are Zero-Shot Learners

    Accessed: 2026-03-23 31 Wei J, Bosma M, Zhao V Y, et al. Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652, 2021 32 Ouyang L, Wu J, Jiang X, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 2022, 35: 27730–27744 33 Xu L, Zhao Y, Zhou D, et al. Pll...

    work page internal anchor Pith review arXiv 2026

  13. [13]

    An empirical study of catastrophic forgetting in large language models during continual fine- tuning, 2025

    32100–32121 40 French R M. Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 1999, 3: 128–135 41 Kirkpatrick J, Pascanu R, Rabinowitz N, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 2017, 114: 3521–3526 42 Luo Y, Yang Z, Meng F, et al. An empirical study of ca...

    work page arXiv 1999

  14. [14]

    E\^ 2vpt: An effective and efficient approach for visual prompt tuning

    105–124 46 Han C, Wang Q, Cui Y, et al. Eˆ 2vpt: An effective and efficient approach for visual prompt tuning. arXiv preprint arXiv:2307.13770, 2023 47 Shen Y, Xu Z, Wang Q, et al. Multimodal instruction tuning with conditional mixture of LoRA. In: Proceedings of Ku L W, Martins A, Srikumar V, editors, Proceedings of the 62nd Annual Meeting of the Associa...

    work page arXiv 2023

  15. [15]

    M 2PT: Multimodal prompt tuning for zero-shot instruction learning

    637–648 48 Wang T, Liu Y, Liang J C, et al. M 2PT: Multimodal prompt tuning for zero-shot instruction learning. In: Proceedings of Al-Onaizan Y, Bansal M, Chen Y N, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing,

    2024

  16. [16]

    Accessed: 2025-01-21

    3723–3740 49 Molchanov P, Tyree S, Karras T, et al. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440, 2016 50 Xie W, Feng Y, Gu S, et al. Importance-based neuron allocation for multilingual neural machine translation. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ...

    work page arXiv 2016

  17. [17]

    Overview of the iwslt 2017 evaluation campaign

    2–17 52 Cettolo M, Federico M, Bentivogli L, et al. Overview of the iwslt 2017 evaluation campaign. In: Proceedings of Proceedings of the 14th International Workshop on Spoken Language Translation,

    2017

  18. [18]

    Pp-ocrv3: More attempts for the improvement of ultra lightweight OCR sys- tem.CoRR, abs/2206.03001, 2022

    2–14 53 Li C, Liu W, Guo R, et al. Pp-ocrv3: More attempts for the improvement of ultra lightweight ocr system. arXiv preprint arXiv:2206.03001, 2022 54 NLLB Team, Costa-juss` a M R, Cross J, et al. No language left behind: Scaling human-centered machine translation. Github.com, 2022 55 Wei J, Wang X, Schuurmans D, et al. Chain-of-thought prompting elicit...

    work page arXiv 2022

  19. [19]

    UMTIT: Unifying recognition, translation, and generation for multimodal text image translation

    24824–24837 56 Niu L, Meng F, Zhou J. UMTIT: Unifying recognition, translation, and generation for multimodal text image translation. In: Proceedings of Calzolari N, Kan M Y, Hoste V, et al., editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024),

    2024

  20. [20]

    Document image machine translation with dynamic multi-pre-trained models assembling

    16953–16972 57 Liang Y, Zhang Y, Ma C, et al. Document image machine translation with dynamic multi-pre-trained models assembling. In: Proceedings of Duh K, Gomez H, Bethard S, editors, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers),

    2024