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Collections including paper arxiv:2401.08406
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WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia
Paper • 2305.14292 • Published -
Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps
Paper • 2312.07796 • Published -
RAGAS: Automated Evaluation of Retrieval Augmented Generation
Paper • 2309.15217 • Published • 3 -
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
Paper • 2210.02627 • 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 • 80 -
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
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Paper • 2211.05100 • Published • 29 -
CsFEVER and CTKFacts: Acquiring Czech data for fact verification
Paper • 2201.11115 • Published -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 17 -
FinGPT: Large Generative Models for a Small Language
Paper • 2311.05640 • Published • 32
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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 • 7 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 51