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Link-Context Learning for Multimodal LLMs

Yan Tai, Weichen Fan, Zhao Zhang, Feng Zhu, Rui Zhao, Ziwei Liu

arXiv:2308.07891v1cs.CVcs.CL

TL;DR

MLLMs still struggle to recognize images and concepts that were unseen during training, and conventional in-context learning does not ensure that they learn image-label relationships. The paper introduces link-context learning, which connects demonstrations causally to inference, and reports that models can map input-label pairs to previously unseen images and concepts. The paper presents this as a foundational baseline focused on MLLMs and basic image-classification tasks.

  • Problem

    MLLMs struggle when both images and labels are unseen, while conventional in-context learning may not teach the causal relationship between image-label pairs.

    Source: §1 · p. 2

  • Method

    LCL trains MLLMs to acquire new concepts from conversation and retain existing knowledge by linking demonstrations causally to inference.

    Source: §1 · p. 2

  • Results

    Experiments show that MLLMs can map ground-truth input-label pairs and generalize this capacity to previously unseen images and novel concepts.

    Source: §6.2 · p. 8

  • Takeaways & Limitations

    The paper presents LCL as a foundational baseline for exploring link-context learning in MLLMs, validated on basic tasks such as image classification.

    Source: §6.1 · p. 8

    The work focuses on MLLM link-context learning and validates basic tasks such as image classification.

    Source: §6.1 · p. 8

Abstract

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The ability to learn from context with novel concepts, and deliver appropriate responses are essential in human conversations. Despite current Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being trained on mega-scale datasets, recognizing unseen images or understanding novel concepts in a training-free manner remains a challenge. In-Context Learning (ICL) explores training-free few-shot learning, where models are encouraged to ``learn to learn" from limited tasks and generalize to unseen tasks. In this work, we propose link-context learning (LCL), which emphasizes "reasoning from cause and effect" to augment the learning capabilities of MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal relationship between the support set and the query set. By providing demonstrations with causal links, LCL guides the model to discern not only the analogy but also the underlying causal associations between data points, which empowers MLLMs to recognize unseen images and understand novel concepts more effectively. To facilitate the evaluation of this novel approach, we introduce the ISEKAI dataset, comprising exclusively of unseen generated image-label pairs designed for link-context learning. Extensive experiments show that our LCL-MLLM exhibits strong link-context learning capabilities to novel concepts over vanilla MLLMs. Code and data will be released at https://github.com/isekai-portal/Link-Context-Learning.

1 Introduction

The paper proposes link-context learning (LCL), which links demonstrations causally to inference so MLLMs can learn novel concepts from conversation. It also introduces ISEKAI, a dataset of unseen image-concept pairs, and reports stronger performance than existing models across almost all its categories.

1 Introduction

  • When both images and labels are unseen, deriving cause and effect from demonstrations to predict accurately remains an unsolved challenge for MLLMs.The paper says acquiring novel concepts does not impede existing knowledge, allowing the model to distinguish original and newly learned images.Source: §1 · p. 2
  • LCL makes demonstrations causally relevant to inference, unlike ICL demonstrations that can involve irrelevant tasks.For example, a demonstration renaming “apple” as “orange” should guide the model to call apple “orange” during inference.Source: §1 · p. 2
  • On ISEKAI, LCL outperforms OpenFlamingo (9B) [6] and Otter (9B) [5] across almost all categories.The comparison covers scenarios involving entirely unseen images.Source: Figure 3 · p. 2
  • ISEKAI evaluates few-shot learning with unseen images and fabricated concepts, addressing the difficulty of evaluating models extensively trained on real-world data.Its images are generated and its labels or concepts are fabricated.Source: §1 · p. 2

2 Related Works

Prior multimodal work includes prompt tuning, instruction tuning, and in-context learning. The paper distinguishes LCL by establishing a causal link between support and query examples, including for unseen images and concepts.

Multimodal Prompt Tuning

  • Prompt tuning methods adapt multimodal models by updating a small number of parameters, but traditional methods are not suited to generative MLLMs.The section names CLIP-based approaches including CoOp [15], CoCoOp [16], and MaPLe.Source: §2 · p. 3

Multimodal Instruction Tuning

  • Instruction-tuned MLLMs show zero-shot capability but still cannot recognize classes absent from training.The related-work section describes instruction tuning across multiple datasets and visual grounding tasks.Source: §2 · p. 3

Multimodal In-Context Learning

  • Unlike prior multimodal ICL, LCL links support and query causally, associating prompts with inference samples and unseen images with new concepts.Prior M-ICL work includes Flamingo [33] and Otter [5].Source: §2 · p. 3

3 Link-Context Learning

Link-context learning (LCL) treats few-shot learning as learning causal links between support examples and queries. The paper adapts this setting to MLLMs through a training strategy designed to teach those links.

3.1 Preliminary

  • LCL predicts query answers by using causal relationships between support-set examples and the query, including rules taught through arithmetic or novel image-classification demonstrations.The paper focuses on applying this training-free, causal-linked few-shot setting to MLLMs.Source: §3.1 · pp. 3–4

3.2 Bring Link-Context Learning to MLLMs

  • The MLLM training approach uses Shikra as its baseline, divides ImageNet1k classes into ImageNet-900 and ImageNet-100, and incorporates contrastive learning.The contrastive component is intended to guide the model toward shared characteristics within a class and distinctions between classes.Source: §3.2 · p. 4

3.2.1 Training Dataset

  • LCL training uses limited samples per ImageNet1k category and selects neighboring categories according to CLIP-based similarities across image and text features.The neighbors are arranged by similarity to help represent diverse image categories for learning source-target links.Source: §3.2.1 · p. 4

3.2.2 Training Strategy

  • The training strategy groups support examples into prototypes and maximizes query-answer likelihood; its [2-way] setup uses two class prototypes and freezes the visual encoder.The [2-way] training samples a positive class and uses a neighboring class as a negative.Source: §3.2.2 · p. 4
  • Training variants extend fixed 16-shot [2-way] training with 2–16-shot sampling, either averaged or weighted, or mix [2-way] tasks evenly with Shikra’s original tasks.The [2-way-random] variant trains for 10 additional epochs; the [mix] variant includes Shikra’s tasks.Source: §3.2.2 · p. 4

4 ISEKAI Dataset

ISEKAI is an evaluation dataset of generated images and fabricated concepts intended to assess LCL on concepts MLLMs are unlikely to have encountered. It pairs novel concepts with related real-world concepts and provides multiple images per concept.

4 ISEKAI Dataset

  • ISEKAI combines generated images of concepts described as unreal and rarely encountered with real-world images, distinguishing its two image sources.Figure 4 contrasts images from the nonexistent “ISEKAI World” with images sourced from reality.Source: §4 · p. 4, Figure 4 · p. 5
  • ISEKAI was created to evaluate MLLMs’ LCL ability, and its concept names and appearance descriptions also support evaluations beyond LCL.The images were generated with Midjourney and manually selected for core concept consistency.Source: §4 · pp. 4–5
  • The dataset contains 20 groups and 40 categories, pairing each new concept with a related real-world concept, with at least 32 images per concept.The dataset is described as continuing to grow, and the paired concepts can serve as challenging negative samples for one another.Source: §4 · p. 5

5 Experiments

Experiments compare LCL-based methods with Shikra and in-context-learning MLLMs on ISEKAI and ImageNet-100, then examine label integrity, shot count, and demonstration position.

5.1 Results on ISEKAI

  • On ISEKAI-10, our model is competitive with OpenFlamingo and Otter across shot numbers; on ISEKAI-pair, it surpasses both.ISEKAI-pair evaluates combinations across categories that do not exist in the real world.Source: §5.1 · pp. 5–6, Table 1 · p. 6

5.2 Results on ImageNet-100

  • On ImageNet-100, whose 100 classes were entirely absent from training, the mix strategy achieved 83% accuracy at 6-shot, versus Otter’s peak 25% and OpenFlamingo’s 78%.Source: §5.2 · p. 6

What does the model’s decision-making in the case of multi-shot depend on?

  • The mix strategy reaches 83% accuracy at 6-shot but falls to 57% at 16-shot; 2-way rises from 51% at 2-shot to 79% at 16-shot.Randomly finetuned strategies improve at lower shots but lose accuracy at higher shots; the authors cite even, sustained, generalized training as effective.Source: What does the model’s decision-making… · pp. 7–8
  • Figure 8’s position-specific accuracy drops indicate reliance on beginning and middle positions; longer contexts also show declining performance, consistent with “lost in the middle.”The authors relate this positional reliance to the plateau at higher shot counts.Source: What does the model’s decision-making… · p. 7, Figure 8 · p. 8

Would the model benefit from using a larger shot?

5.3 Ablation Study

  • At a 100% false rate, our model reaches 0% accuracy versus OpenFlamingo’s 38%; accuracy falls from 0.78 to 0.00 as label correctness drops from 100% to 0%.The ablation gradually inserts false labels into the support set.Source: §5.3 · p. 6, Figure 7 · p. 8

What does the model’s decision-making in the case of multi-shot depend on?

6 Discussion

The paper presents LCL as a way for MLLMs to acquire and apply knowledge about unseen images and novel concepts in single conversations. It frames this work as a foundational baseline and identifies deeper analysis of causal links as future work.

6.1 Limitations

  • The work focuses on MLLMs and validates basic tasks such as image classification, so its findings do not yet establish broader task coverage.The authors characterize it as a foundational baseline for exploring link-context learning.Source: §6.1 · p. 8
  • Further theoretical analysis is needed to examine causal relationships among support samples and between the support set and query.Source: §6.1 · p. 8
  • A unified implementation of in-context learning for MLLMs and LLMs is identified as a future direction.Source: §6.1 · p. 8

6.2 Conclusion

  • LCL demonstrates that MLLMs can map ground-truth input-label pairs and generalize to previously unseen images and novel concepts within single conversations.The authors describe this as a foundational baseline for exploring link-context learning.Source: §6.2 · p. 8

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