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What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration

Libo Qin, Qiguang Chen, Hao Fei, Zhi Chen, Min Li, Wanxiang Che

arXiv:2410.20482v1cs.CLcs.AIcs.CV

TL;DR

The factors underlying MM-ICL effectiveness remain under-explored. This paper investigates retrieval, ordering, and prompt construction across models and tasks, finding that multimodal retrieval, intra-demonstration ordering, and introductory instructions are consequential. It also identifies limits in the modalities and image instructions considered.

  • Problem

    Although MM-ICL can adapt to downstream tasks without parameter tuning, the factors underlying its effectiveness remain under-explored.

    Source: §1 · pp. 1–2

  • Method

    The study tests 20 strategies across retrieval, ordering, and prompt construction using six vision large language models across four tasks.

    Source: §1 · p. 2

  • Results

    Across the investigated factors, the study identifies multimodal retrieval, intra-demonstration ordering, and introductory instructions as important to MM-ICL performance.

    Source: §1 · p. 2, §8 · p. 10

  • Takeaways & Limitations

    The findings offer guidance for refining understanding of MM-ICL mechanisms and developing future MM-ICL strategies.

    Source: §8 · p. 10

    The study is limited to image and text modalities and gives insufficient consideration to complex image instructions such as grounding or added arrows.

    Source: §7 · p. 10

Abstract

from arXiv · show

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks without requiring additional parameter tuning. However, the underlying rules for the effectiveness of MM-ICL remain under-explored. To fill this gap, this work aims to investigate the research question: "What factors affect the performance of MM-ICL?'' To this end, we investigate extensive experiments on the three core steps of MM-ICL including demonstration retrieval, demonstration ordering, and prompt construction using 6 vision large language models and 20 strategies. Our findings highlight (1) the necessity of a multi-modal retriever for demonstration retrieval, (2) the importance of intra-demonstration ordering over inter-demonstration ordering, and (3) the enhancement of task comprehension through introductory instructions in prompts. We hope this study can serve as a foundational guide for optimizing MM-ICL strategies in future research.

1 Introduction

Although MM-ICL can adapt to downstream tasks without parameter tuning, the factors underlying its effectiveness remain under-explored. This paper studies retrieval, ordering, and prompt construction to identify performance determinants.

1 Introduction

  • The study addresses the gap in understanding which factors influence MM-ICL effectiveness, beyond prior work focused on optimization.It frames this as a systematic investigation of performance determinants.Source: §1 · pp. 1–2
  • Experiments examine 20 strategies across four tasks using six representative vision large language models.Source: §1 · p. 2
  • The findings identify multi-modal alignment as a bottleneck, intra-demonstration ordering as more important than inter-demonstration ordering, and introductory instructions as beneficial for task understanding.Introductory instructions outperform summative and intra-demonstration instructions in the reported comparison.Source: §1 · p. 2

2 Background

MM-ICL prompt building is presented as a three-stage process: retrieve demonstrations, order them, then construct a structured prompt.

2 Background

  • Demonstration retrieval selects a subset of relevant multimodal examples for a test query from a validation dataset.Source: §2 · p. 2
  • Demonstration ordering rearranges the retrieved examples into a sequence for prompt construction.The paper notes that demonstration order can affect model performance.Source: §2 · p. 2
  • Prompt construction transforms ordered demonstrations into a structured prompt, incorporating delimiters and instructions.Source: §2 · p. 2

3 What Factors Affect Multi-modal In-Context Learning?

The paper studies MM-ICL through three design stages: retrieval, ordering, and prompt construction. It analyzes representation, comparison, and selection for retrieval; modality and example sequences for ordering; and instruction placement for prompts.

3.1 Exploration of MM-ICL Demonstration Retrieval

  • Retrieval analysis varies sample representations, comparison metrics, and selection criteria to study their effects on MM-ICL performance.Representations include visual, textual, and multimodal encoders; comparisons include cosine, L2, and semantic diversity, while selection examines domain, image style, and token distance.Source: §3.1 · p. 3

3.2 Exploration of MM-ICL Demonstration Ordering

  • Ordering analysis examines both modality sequences within each demonstration and the arrangement of demonstrations in the retrieved set.Tested intra-demonstration configurations include text-image-text, text-text-image, and image-text-text.Source: §3.2 · pp. 3–4

3.3 Exploration of MM-ICL Prompt Construction

  • Prompt construction compares task instructions placed before demonstrations, after examples, or within each demonstration.Introductory instructions provide an overview; summative instructions follow examples, while intra-demonstration instructions are embedded in each example.Source: §3.3 · pp. 4–5, Figure 4 · p. 4

4 Experimental Setup

Experiments cover four MM-ICL tasks with task-specific evaluation metrics, using a controlled retrieval baseline and fixed inference settings.

4 Experimental Setup

  • The evaluation covers image captioning, visual question answering, image classification, and chain-of-thought reasoning, with two metrics for each task.Metrics are CIDEr and BERTScore for captioning; Token-F1 and BERTScore for VQA; accuracy and F1 for classification; and accuracy and reasoning alignment score for reasoning.Source: §4 · p. 5
  • The retrieval baseline uses a multimodal encoder, cosine similarity, same-task retrieval, delimiters, and a 3-shot setting.Source: §4 · p. 5
  • Open-source models run on two A100 80G GPUs, with top-p chosen from 0.95 or 1 and temperature varied from 0 to 1.The paper identifies temperature as its main error variable.Source: §4 · p. 5

5 Empirical Analysis of Factors Affecting MM-ICL

The experiments examine retrieval, demonstration ordering, and prompt construction, finding that multi-modal alignment, intra-demonstration ordering, and introductory instructions are important factors in MM-ICL performance.

5.1.1 Sample Representation

  • Multi-modal retrieval consistently beats zero-shot, random, and single-modality methods, but text retrieval works well for classification and reasoning; alignment matters more than scaling.The comparison covers retrievers using different modal representations.Source: §5.1.1 · p. 5, Table 1 · p. 6
  • MM-ICL relies more on semantic than token-pattern representations, while relevant demonstrations add 3.84% over random versus over 10% for text-only ICL.The paper concludes that multi-modal context reduces the need for careful demonstration selection compared with text-only ICL.Source: §5.1.1 · pp. 5–6

5.1.2 Sample Comparison

  • Cosine similarity outperforms L2, whereas semantic diversity produces limited improvement in MM-ICL.Source: §5.1.2 · p. 7

5.1.3 Sample Selection

  • Out-of-domain demonstrations lower performance nearly 4%; visual style helps only VQA, and optimal token distance varies by task.Source: §5.1.3 · pp. 7–8

5.2 Empirical Analysis of MM-ICL Demonstration Ordering

  • Image-first intra-demonstration ordering improves performance, while inter-demonstration sequence has negligible impact.Source: §5.2 · p. 8

5.3 Empirical Analysis of MM-ICL Prompt Construction

  • Introductory instructions stably enhance performance, whereas summative and intra-demonstration instructions generally decrease it.Source: §5.3 · pp. 8–9
  • More demonstrations help captioning and VQA but hurt beyond three; reasoning gains are absent, and removing delimiters causes only minor decline.For complex reasoning, the authors cite cognitive overload and argue that quality can outweigh quantity.Source: §5.3 · p. 9

6 Related Work

Prior MM-ICL work developed training templates, task-specific adaptations, and prompt-optimization approaches, while this study examines factors affecting effectiveness across models, factors, and tasks.

6 Related Work

  • Training-stage templates and task-specific adaptations broadened MM-ICL across domains and high-definition images.Prior work also explored applications including scene-text recognition, image generation, and game instructions.Source: §6 · p. 9
  • Prompt optimization and textualization of visual information were used to improve task performance, alongside datasets and instruction-modification studies.The cited work includes visual reasoning and multi-modal classification.Source: §6 · p. 10
  • Unlike earlier naive retrieval explorations by Baldassini et al. [2024] and Chen et al. [2024c], this study systematically examines MM-ICL factors across 6 VLLMs, 20 factors, and 4 tasks.The authors frame this analysis as practical guidance for future research.Source: §6 · p. 10

7 Discussion

The authors aim to improve understanding of MM-ICL mechanisms and inform future model development, while noting limits in modalities and image-instruction coverage.

7 Discussion

  • The study aims to clarify MM-ICL mechanisms and guide future developments, with possible relevance to multimodal models and applications.These are stated aims and potential impacts, not reported measured effects.Source: §7 · p. 10
  • The exploration is limited to image and text modalities, leaving video and multilingual MM-ICL for future research.The authors attribute this scope limit to time and cost constraints.Source: §7 · p. 10
  • Grounding and arrow-inclusive image instructions receive insufficient consideration because they require more complex human input and are poorly supported by most current models.Source: §7 · p. 10

8 Conclusion

The conclusion identifies retrieval, within-demonstration ordering, and task-specific instructions as performance determinants; the baseline uses multimodal representations and cosine similarity.

8 Conclusion

  • Multi-modal retrieval outperforms single-modal approaches, while intra-demonstration ordering and task-specific prompt instructions affect MM-ICL performance.The conclusion reports experiments with 6 models, 20 factors, and 4 tasks.Source: §8 · p. 10
  • The authors present these findings as guidance for understanding MM-ICL mechanisms and developing more effective approaches.Source: §8 · p. 10

A The Implement Details for Standard Baseline

A.1 Demonstration Retrieval Implementation for Baseline

The baseline represents samples in a shared multimodal embedding space, ranks same-task candidates by cosine similarity, and selects the top-k samples.

A.1 Demonstration Retrieval Implementation for Baseline

  • BridgeTower represents samples in a unified embedding space integrating visual and textual information, with emphasis on capturing image semantics.Source: §A.1 · p. 17
  • Cosine similarity compares query and candidate embedding vectors, and retrieval is confined to the same task and domain before ranking candidates.Higher similarity scores determine the ranking, and the top-k samples are selected.Source: §A.1 · pp. 17–18
  • For each task, the M3IT and M3CoT datasets were evenly sampled to 500 items per task.The table identifies image captioning, classification, visual question answering, and chain-of-thought reasoning tasks.Source: Table 2 · p. 17

A.2 Demonstration Ordering Implementation for Baseline

The appendix specifies baseline demonstration ordering and prompt construction, including within-demonstration modality order, inter-demonstration ranking, and a three-example prompt format.

A.2 Demonstration Ordering Implementation for Baseline

  • The baseline places text after each image within a demonstration and ranks demonstrations by similarity to the query.The similarity metric assesses alignment of key features between query and demonstrations.Source: §A.2 · p. 18

A.3 Prompt Construction Implementation for Baseline

  • The prompt uses a delimiter and three examples per task, intended to separate input segments and provide contextual information without excessive examples.The authors describe the three-shot setting as balancing sufficient context against potential cognitive overload.Source: §A.3 · p. 18

A.4 Baseline Prompt

  • The baseline prompt lists textual inputs for demonstrations 1–3 and the query, alongside a multimodal encoder, cosine similarity, and task-specific visual retrieval.The baseline also uses a delimiter and a three-shot setting.Source: §A.4 · pp. 18–19

B The Implement Details for Sample Comparison

This section describes cosine similarity, negative Euclidean distance, and semantic diversity as sample-comparison approaches for retrieval.

B.1 Metric Calculation

  • Cosine similarity compares query and sample representations, while L2 similarity is the negative Euclidean distance between them.The negative distance represents similarity, so a higher value indicates greater similarity.Source: §B.1 · p. 19
  • The diversity retriever first selects the 10 most relevant samples, then chooses 3 with the highest semantic diversity.The approach is attributed to Li and Qiu [2023b].Source: §B.1 · p. 19

B.2 Comparison and Analysis

This section compares similarity and diversity metrics, describes prompt-construction choices, and reports prompt-robustness, reproducibility, and checklist disclosures. It covers the authors’ statements on compute resources, ethics, broader impacts, safeguards, assets, and human-subject research.

B.2 Comparison and Analysis

  • Cosine similarity is superior to L2 similarity in MM-ICL, while diversity yields limited improvement there despite helping text-based in-context learning.Cosine emphasizes directional semantic alignment, whereas L2 measures more complete alignment; the authors suggest multimodal interactions may make diversity alone insufficient.Source: §B.2 · pp. 19–20, Figure 12 · p. 20, Figure 13 · p. 20

C.1 The Implement Details for Demonstration Sampling

  • The sampling process selects k′ retrieved demonstrations for the prompt, and the experiments vary k′ to examine its effect on MM-ICL performance.Source: Figure 14 · p. 21, §C.1 · p. 21

C.2 The Implement Details for Delimiter Injection

  • Delimiter injection places [Request] before each demonstration input and [Response] before its output to distinguish them in the prompt.Source: §C.2 · p. 21, Figure 15 · p. 21

C.3 The Implement Details for Instruction Injection

  • The instruction methods place task instructions before demonstrations, after them, or within each demonstration as introductory, summative, and intra-demonstration instructions.Source: §C.3 · p. 23

D Prompt Robust

  • Different prompts with semantically equivalent content but varied wording have minimal influence on results, suggesting the findings are robust to prompt formulation.Source: Table 4 · p. 24, D Prompt Robust · p. 24

NeurIPS Paper Checklist

  • The checklist reports detailed experimental setup descriptions for reproducibility, but notes that error bars are omitted for some tasks because human annotation and computation are costly.Source: NeurIPS Paper Checklist · pp. 26–27
  • The authors answer yes on compute-resource information and NeurIPS Code of Ethics compliance.They say Section 4 outlines the model compute resources provided and state that they comply with the Code of Ethics.Source: NeurIPS Paper Checklist · p. 28

10. Broader Impacts

  • The authors answer yes on broader impacts, saying they addressed them in Section 7 but omitted a harmfulness discussion because the empirical exploration poses no additional social risks.Source: §10 · p. 28

11. Safeguards

  • The authors mark safeguards not applicable, stating that the paper poses no such risks.Source: §11 · p. 29

12. Licenses for existing assets

  • The authors mark licensing for existing assets not applicable because the paper does not use existing assets.Source: §12 · p. 29

13. New Assets

  • The authors mark new-asset documentation not applicable because the paper releases no new assets.Source: §13 · p. 30

14. Crowdsourcing and Research with Human Subjects

  • The authors mark crowdsourcing, human-subject research, and IRB items not applicable because the paper involves neither crowdsourcing nor human subjects.Source: §14 · p. 30

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