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Collections including paper arxiv:2402.05672
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Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 79 -
Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
Paper • 2405.06932 • Published • 16 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47 -
Multilingual E5 Text Embeddings: A Technical Report
Paper • 2402.05672 • Published • 19
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Text Embeddings by Weakly-Supervised Contrastive Pre-training
Paper • 2212.03533 • Published • 1 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 79 -
Generative Representational Instruction Tuning
Paper • 2402.09906 • Published • 51
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LongAlign: A Recipe for Long Context Alignment of Large Language Models
Paper • 2401.18058 • Published • 21 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 16 -
Scavenging Hyena: Distilling Transformers into Long Convolution Models
Paper • 2401.17574 • Published • 15 -
Rethinking Interpretability in the Era of Large Language Models
Paper • 2402.01761 • Published • 21
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DocLLM: A layout-aware generative language model for multimodal document understanding
Paper • 2401.00908 • Published • 177 -
Multilingual E5 Text Embeddings: A Technical Report
Paper • 2402.05672 • Published • 19 -
Accelerating High-Fidelity Waveform Generation via Adversarial Flow Matching Optimization
Paper • 2408.08019 • Published • 9
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LLaMA Beyond English: An Empirical Study on Language Capability Transfer
Paper • 2401.01055 • Published • 53 -
YAYI 2: Multilingual Open-Source Large Language Models
Paper • 2312.14862 • Published • 13 -
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
Paper • 2312.06134 • Published • 2 -
TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes
Paper • 2311.10797 • Published
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Dense X Retrieval: What Retrieval Granularity Should We Use?
Paper • 2312.06648 • Published • 1 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 79 -
Text Embeddings Reveal (Almost) As Much As Text
Paper • 2310.06816 • Published • 1 -
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Paper • 2401.08406 • Published • 37