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Cross-Modal Consistency in Multimodal Large Language Models

Xiang Zhang, Senyu Li, Ning Shi, Bradley Hauer, Zijun Wu, Grzegorz Kondrak, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan

arXiv:2411.09273v1cs.CLcs.AI

TL;DR

Prior evaluations often assess multimodal models one modality at a time, leaving unanswered whether identical task instances yield comparable performance across modalities. This paper defines cross-modal consistency and evaluates it with parallel vision-language datasets; GPT-4V shows substantial vision-language inconsistency, and the authors identify information-preserving conversion as a boundary on which tasks can be tested.

  • Problem

    Prior work often evaluates modalities in isolation, leaving unclear whether models perform similarly on identical task instances across modalities and how modalities relate.

    Source: §1 · p. 1

  • Method

    The paper defines cross-modal consistency and evaluates it with a quantitative framework and parallel vision-language datasets spanning seven tasks.

    Source: §1 · p. 2

  • Results

    GPT-4V exhibits significant inconsistency between vision and language, with performance varying across modalities on the same task instances.

    Source: §1 · p. 2, §7 · p. 8

  • Takeaways & Limitations

    The findings offer guidance for using multimodal models and point to the need for more integrated system designs.

    Source: §1 · p. 2, §7 · p. 8

    Some tasks, such as detecting emotions from speech, may lack an information-preserving converter, preventing ready cross-modal evaluation on every dataset.

    Source: Limitations · p. 9

Abstract

from arXiv · show

Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual content. Models like GPT-4V, which merge computer vision with advanced language processing, exhibit extraordinary proficiency in handling intricate tasks that require a simultaneous understanding of both textual and visual information. Prior research efforts have meticulously evaluated the efficacy of these Vision Large Language Models (VLLMs) in various domains, including object detection, image captioning, and other related fields. However, existing analyses have often suffered from limitations, primarily centering on the isolated evaluation of each modality's performance while neglecting to explore their intricate cross-modal interactions. Specifically, the question of whether these models achieve the same level of accuracy when confronted with identical task instances across different modalities remains unanswered. In this study, we take the initiative to delve into the interaction and comparison among these modalities of interest by introducing a novel concept termed cross-modal consistency. Furthermore, we propose a quantitative evaluation framework founded on this concept. Our experimental findings, drawn from a curated collection of parallel vision-language datasets developed by us, unveil a pronounced inconsistency between the vision and language modalities within GPT-4V, despite its portrayal as a unified multimodal model. Our research yields insights into the appropriate utilization of such models and hints at potential avenues for enhancing their design.

1 Introduction

Evaluations of multimodal models often score each modality separately, leaving their cross-modal relationship unexamined. The paper defines cross-modal consistency and evaluates GPT-4V on seven parallel vision-language tasks, finding substantial differences between its vision and language performance.

1 Introduction

  • Distinct training methods and differences in modality-specific data quality are associated with performance disparities across modalities.The passage names contrastive learning, embodied image-language modeling, and varying training-data quality.Source: §1 · p. 1
  • Separate modality evaluations do not establish whether a model performs similarly on identical task instances or how one modality influences another.A model may excel at vision tasks yet lag in language understanding.Source: §1 · pp. 1–2
  • The paper introduces cross-modal consistency and a quantitative framework to compare capabilities across modalities, especially vision and language.The authors describe consistency as important for coherence, reliability, interpretability, and user trust.Source: §1 · p. 2
  • GPT-4V shows significant inconsistency across vision and language on seven parallel tasks, with performance varying when identical instances are prompted in different modalities.The authors introduce Vision-Depicting-Prompting as a potential remedy.Source: §1 · p. 2

2 Related Work

Prior evaluations have tested multimodal models across specialized and general vision tasks, but often report per-dataset performance without examining vision-language gaps. The paper also situates cross-modal consistency alongside prior work on cross-lingual performance differences and within-modality consistency.

2 Related Work

  • Evaluations of GPT-4V cover specialized domains and general skills, yet often provide little analysis of relative vision-language capability gaps.Examples include biomedicine, medical applications, autonomous driving, chart understanding, and OCR.Source: §2 · p. 2
  • Prior cross-lingual analyses found task-performance disparities across languages, including an English bias in models such as GPT-3.5 [Zhang et al., 2023b].Chou et al. (2024) examine consistency within each modality for multimodal models.Source: §2 · p. 2
  • This paper extends consistency analysis across modalities and treats different languages as distinct modalities within its generalized framework.The framework is presented as a way to study model consistency across diverse modalities.Source: §2 · p. 2

3 Preliminaries and Key Concepts

The paper frames cross-modal consistency as a model producing the same output for equivalent task information regardless of modality. Its formulation uses paired data-and-query instances and assumes an information-preserving converter between modalities for each query.

3 Preliminaries and Key Concepts

  • A task instance pairs data from a modality with an abstract query; holding the query fixed defines corresponding task sets across modalities.The data may be an equation or question description in image, text, or speech form.Source: §3 · p. 3
  • A query-specific converter maps data between modalities while preserving information needed to solve the task, so the paired instances share a gold label.The framework assumes such a converter exists for every query, although finding one is beyond the paper's scope; some information, such as speech emotion, may be difficult to preserve.Source: §3 · p. 3
  • The model maps modality data and queries to answers, with a collective input space spanning relevant modalities and a unified answer space.For GPT-4V, the answer space takes the form of text.Source: §3 · p. 3
  • A model is cross-modally consistent when its output remains invariant under a modality transformation that preserves task-relevant information.For example, converting a mathematical equation between image and text should not change the model's answer.Source: §3 · p. 3, Figure 2 · p. 3

4 Method

The method evaluates vision-language consistency by constructing paired instances with an information-preserving converter, querying the model on both forms, and aggregating pairwise results into a task score.

4 Method

  • The evaluation framework tests cross-modal consistency quantitatively, focusing on vision and language and specifying methodology and evaluation metrics.Source: §4 · p. 3

4.1 WorkFlow

  • The workflow converts each task instance into a parallel modality while retaining its query and gold label, then evaluates both versions independently.For image-to-text conversion, the authors use OCR with human verification; for text-to-image, they use screenshots, selecting tasks where required information can be retained.Source: §4.1 · p. 4

4.2 Metrics

  • The task consistency score C_t is the proportion of instances on which the model performs consistently across the two modalities.Source: §4.2 · p. 4

5 Experiments

The experiments evaluate GPT-4V on seven parallel vision-language tasks and find substantial differences between image and text performance despite equivalent task information.

5.1 Data Construction

  • The authors built seven parallel vision-language task datasets, preserving task information across image/text conversions and manually checking conversions for errors.The datasets were created because no parallel vision-language task dataset existed.Source: §5.1 · p. 4

5.1.1 Task Description.

  • Math Equation Solving pairs rendered LaTeX with text questions from 901 high-school problems and 50 college calculus problems; GSM8K and LogicQA questions are converted to images.The paired math questions represent two difficulty levels.Source: §5.1.1 · p. 4, Figure 3 · p. 5
  • The suite also includes 30 generated numerical tables, shuffled state-machine transition rules, and MMLU passages rendered as images for paired comprehension tests.State-machine questions vary the number of states and steps; MMLU passages are paired with multiple-choice questions.Source: §5.1.1 · p. 5

5.2 Experiment Details.

  • GPT-4V classical mode was tested on 50 randomly selected samples per dataset, with image-only vision prompts and answers requested without reasoning.The model's classical mode excludes additional plug-ins and uses a relatively low decoding temperature.Source: §5.2 · pp. 5–6

5.3 Main Results

  • Across seven paired tasks, GPT-4V shows substantial image/text performance disparities despite equivalent task information, with language outperforming vision.The paper reports that image performance also declines further on reasoning tasks and information-extraction tasks.Source: §5.3 · p. 6, Table 1 · p. 6
  • 0.92 consistency on math reasoning occurred despite both modalities having accuracy ≤0.40, while LogicQA consistency was 0.64 despite modality accuracy ≥0.44.The authors note that response agreement can be high or low regardless of per-modality accuracy.Source: §5.3 · p. 6

5.4 Ablation Study on Content Extraction from Images

  • OCR accuracy approached nearly 100% for LogicQA, MMLU, and Table Understanding, while the image/text accuracy gap reached up to 90%; authors attribute the gap to internal reasoning.The OCR test was used to examine whether difficulty accessing image information explained the performance gap.Source: §5.4 · p. 7
  • Math-equation OCR was lower, and conditional consistency scores showed no direct correlation with OCR accuracy.The authors use this result to support their claim of distinct internal behavior across modalities.Source: §5.4 · p. 7, Table 3 · p. 7

6 Vision-Depicting-Prompting (VDP)

Vision-Depicting-Prompting (VDP) converts image tasks into text descriptions before answering with both the description and original image. Experiments report improved accuracy and cross-modal consistency, with performance reaching text-prompting parity on understanding tasks.

6 Vision-Depicting-Prompting (VDP)

  • VDP addresses the reported advantage of text-form questions over image-form questions on the same task, even when images contain the needed information.Source: §6 · p. 7

6.1 Prompting Details

  • VDP first prompts the model to describe the image task in text, then answers using both that description and the original image.Source: §6.1 · pp. 7–8
  • VDP does not augment the task information; it uses textual signals to leverage the model’s reported language-modality reasoning bias.Source: §6.1 · p. 8

6.2 Experiment Results for VDP

  • The evaluation applies VDP to five tasks that showed notable image-versus-text performance disparities.Source: §6.2 · p. 8
  • VDP improves image-modality accuracy by more than 12% compared with naive prompting.The reported vision-task accuracy gain exceeds 12%.Source: §6.2 · p. 8
  • Reasoning tasks gain an average 19% accuracy, but VDP still trails text-based prompting.Source: §6.2 · p. 8
  • Understanding tasks gain an average 57% accuracy, including a 90% boost for table understanding, reaching parity with text prompting.Source: §6.2 · p. 8
  • VDP raises consistency scores from 0.64 to 0.80 on LogicQA and from 0.10 to 0.90 on TU versus naive prompting.Source: §6.2 · p. 8

7 Conclusion

The paper analyzes cross-modal consistency and reports that GPT-4V’s visual and textual reasoning representations are relatively independent. It also introduces VDP as a response to the observed inconsistency.

7 Conclusion

  • The analysis finds GPT-4V’s reasoning representations for visual and textual signals to be relatively independent, with language modeling outperforming reasoning in visual contexts.Source: §7 · p. 8
  • The study systematically analyzes cross-modal consistency in multimodal systems.Source: §7 · p. 8
  • The paper introduces Vision-depicting-Prompting as a solution to address modality inconsistency.Source: §7 · p. 9

Limitations

The framework depends on converting task instances between modalities without losing relevant information, which is not feasible for every task.

Limitations

  • The authors describe the method as straightforward and effective in revealing cross-modal inconsistency, while acknowledging challenges on some existing tasks.Source: Limitations · p. 9
  • Some tasks, such as detecting emotions from speech, may lack an information-preserving converter, preventing their ready evaluation for cross-modal consistency.Source: Limitations · p. 9
  • The approach evaluates tasks that can be converted across modalities without information loss, as demonstrated by the constructed datasets.Source: Limitations · p. 9

Ethical Consideration

The paper connects modality-consistency analysis with transparency, interpretability, trust, and ethical and societal considerations in multimodal decision-making.

Ethical Consideration

  • Aligning responses across modalities can reveal insights into model decision-making and rationale, supporting interpretability.Source: Ethical Consideration · p. 9
  • The authors link transparency to trust when multimodal models are used in pivotal decision-making processes.Source: Ethical Consideration · p. 9
  • The discussion calls for continued ethical consideration and proactive measures as multimodal reasoning advances.Source: Ethical Consideration · p. 9

A Math Equation Solving (Easy) Dataset

The easy math-equation dataset presents sample instances in both image and text form, including a text prompt that asks for an equation answer without steps.

A Math Equation Solving (Easy) Dataset

B Math Equation Solving (Hard) Dataset

The supplied appendix material shows two sample instances in image and text form for each of six datasets, from hard math equation solving through state machines.

B Math Equation Solving (Hard) Dataset

C LogicQA Dataset

D MMLU Dataset

E Table Understanding Dataset

F Math Reasoning Dataset

G State Machine Dataset

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