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Dissecting In-Context Learning of Translations in GPTs
Paper • 2310.15987 • Published • 5 -
In-Context Learning Creates Task Vectors
Paper • 2310.15916 • Published • 42 -
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Paper • 2202.07922 • Published • 1 -
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques
Paper • 2310.08101 • Published • 2
Collections
Discover the best community collections!
Collections including paper arxiv:2403.15371
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LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 24 -
PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 57 -
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
Paper • 2403.09704 • Published • 31 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 67
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Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 44 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 36 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 60
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Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 36 -
PIQA: Reasoning about Physical Commonsense in Natural Language
Paper • 1911.11641 • Published • 2 -
AQuA: A Benchmarking Tool for Label Quality Assessment
Paper • 2306.09467 • Published • 1
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Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
GaussianCube: Structuring Gaussian Splatting using Optimal Transport for 3D Generative Modeling
Paper • 2403.19655 • Published • 18 -
WavLLM: Towards Robust and Adaptive Speech Large Language Model
Paper • 2404.00656 • Published • 10 -
Enabling Memory Safety of C Programs using LLMs
Paper • 2404.01096 • Published • 1
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Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Paper • 2403.15042 • Published • 25 -
BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 24 -
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
Paper • 2404.12387 • Published • 38
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Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models
Paper • 2311.00871 • Published • 2 -
Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
Paper • 2205.05055 • Published • 2 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 36
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Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
Paper • 2403.09029 • Published • 54 -
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 24 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 67 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 75
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Contrastive Decoding Improves Reasoning in Large Language Models
Paper • 2309.09117 • Published • 37 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 104 -
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Paper • 2403.14624 • Published • 51 -
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Paper • 2402.12875 • Published • 13