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The paper proposes LLMAE, a method to repurpose pre-trained decoder-only LLMs as continuous text autoencoders using a latent bottleneck, achieving high-fidelity reconstruction and enabling downstream tasks like image captioning.
OpenAI presents a contrastive pre-training approach for generating high-quality text and code embeddings at scale without supervision, achieving state-of-the-art results on linear-probe classification, semantic search, and code search benchmarks.