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Collections including paper arxiv:2311.07590
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Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure
Paper • 2311.07590 • Published • 16 -
Exponentially Faster Language Modelling
Paper • 2311.10770 • Published • 118 -
AppAgent: Multimodal Agents as Smartphone Users
Paper • 2312.13771 • Published • 51 -
DocLLM: A layout-aware generative language model for multimodal document understanding
Paper • 2401.00908 • Published • 181
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Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure
Paper • 2311.07590 • Published • 16 -
A Survey on Language Models for Code
Paper • 2311.07989 • Published • 21 -
Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation
Paper • 2311.08877 • Published • 6 -
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
Paper • 2312.12436 • Published • 13
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A Survey on Language Models for Code
Paper • 2311.07989 • Published • 21 -
The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4
Paper • 2311.07361 • Published • 12 -
Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure
Paper • 2311.07590 • Published • 16 -
Model Cards for Model Reporting
Paper • 1810.03993 • Published • 3
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BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 96 -
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 74 -
In-Context Learning Creates Task Vectors
Paper • 2310.15916 • Published • 41 -
Matryoshka Diffusion Models
Paper • 2310.15111 • Published • 40