Vector-ICL: In-context Learning with Continuous Vector Representations
Keywords: large language models, in-context learning
TL;DR: We discover that large language models can effectively process and in-context learn from continuous representations from various domains, often outperforming regular ICL and domain-specific models across diverse tasks and modalities.
Abstract: Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from black-box pretrained encoders. By aligning input data with an LLM's embedding space through lightweight projectors, we observe that LLMs can effectively process and learn from these projected vectors, which we term Vector-ICL. In particular, we find that pretraining projectors with general language modeling objectives enables Vector-ICL, while task-specific finetuning further enhances performance. In our experiments across various tasks and modalities, including text reconstruction, numerical function regression, text classification, summarization, molecule captioning, time-series classification, graph classification, and fMRI decoding, Vector-ICL often surpasses both few-shot ICL and domain-specific model or tuning. We further conduct analyses and case studies, indicating the potential of LLMs to process vector representations beyond traditional token-based paradigms.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 1196
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