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Can LLMs Reason Over Non-Text Modalities in a Training-Free Manner? A Case Study with In-Context Representation Learning

Created by
  • Haebom

Author

Tianle Zhang, Wanlong Fang, Jonathan Woo, Paridhi Latawa, Deepak A. Subramanian, Alvin Chan

Outline

This paper studies test-time computations that leverage external tools and other deep learning models to improve the performance of large-scale language models (LLMs). While existing methods for integrating non-text modal representations into LLMs require costly supervised learning, our proposed In-Context Representation Learning (ICRL) adaptively utilizes non-text modal representations through small-shot learning. Unlike existing contextual learning, ICRL uses representations of underlying models (FMs) instead of text-label pairs to perform multimodal inference without fine-tuning. We evaluate ICRL's feasibility on several molecular tasks and investigate how FM representations are mapped onto LLMs, factors affecting ICRL performance, and the mechanisms underlying ICRL's effectiveness. ICRL is the first learning-free framework to integrate non-text modal representations into text-based LLMs, offering a promising direction for adaptive multimodal generalization.

Takeaways, Limitations

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Takeaways:
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We present ICRL, a novel framework for integrating non-text modality representations into text-based LLM without training.
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Few-shot learning can improve adaptability to different modalities and domains.
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Multimodal inference is possible without fine-tuning, increasing efficiency.
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We verify the effectiveness of ICRL through experiments in the molecular domain.
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Limitations:
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Currently, only the molecular domain is being evaluated, and further research is needed to determine generalizability to other domains.
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A more in-depth analysis of the factors affecting ICRL performance is needed.
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Further research is needed on compatibility and performance differences with different types of base models.
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Due to the limitations of the learning-free method, performance on certain tasks may be lower than that of supervised learning-based methods.
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