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Towards General Text Embeddings with Multi-stage Contrastive Learning
Paper • 2308.03281 • Published • 2 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 14 -
EELBERT: Tiny Models through Dynamic Embeddings
Paper • 2310.20144 • Published • 3 -
Dynamic Word Embeddings for Evolving Semantic Discovery
Paper • 1703.00607 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2401.00368
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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Paper • 2202.07922 • Published • 1 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 19 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4
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In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 82 -
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion
Paper • 2312.14327 • Published • 8 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 52
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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 76 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 80 -
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 65
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When can transformers reason with abstract symbols?
Paper • 2310.09753 • Published • 4 -
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Paper • 2310.09520 • Published • 12 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 53
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LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Paper • 2309.12307 • Published • 88 -
LMDX: Language Model-based Document Information Extraction and Localization
Paper • 2309.10952 • Published • 65 -
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper • 2310.09263 • Published • 40 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 97
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DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Paper • 2309.03883 • Published • 35 -
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
Paper • 2401.01325 • Published • 27 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 80 -
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
Paper • 2501.12895 • Published • 57
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Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts
Paper • 2309.07430 • Published • 27 -
MindAgent: Emergent Gaming Interaction
Paper • 2309.09971 • Published • 13 -
Cure the headache of Transformers via Collinear Constrained Attention
Paper • 2309.08646 • Published • 13 -
Contrastive Decoding Improves Reasoning in Large Language Models
Paper • 2309.09117 • Published • 39
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FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Paper • 2309.04663 • Published • 6 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 87 -
Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation
Paper • 2310.08541 • Published • 18 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 19