Collections
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Collections including paper arxiv:2404.02078
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Simple and Scalable Strategies to Continually Pre-train Large Language Models
Paper • 2403.08763 • Published • 48 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 99 -
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
Paper • 2403.20041 • Published • 34 -
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 41
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Evaluating Very Long-Term Conversational Memory of LLM Agents
Paper • 2402.17753 • Published • 17 -
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Paper • 2402.16671 • Published • 26 -
Do Large Language Models Latently Perform Multi-Hop Reasoning?
Paper • 2402.16837 • Published • 24 -
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Paper • 2402.15000 • Published • 22
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Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 50 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 46 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 126 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18
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Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
Paper • 2310.04406 • Published • 8 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 91 -
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Paper • 2402.09320 • Published • 6 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 104
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Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Paper • 2402.14848 • Published • 18 -
Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 46 -
How Far Are We from Intelligent Visual Deductive Reasoning?
Paper • 2403.04732 • Published • 18 -
Learning to Reason and Memorize with Self-Notes
Paper • 2305.00833 • Published • 4
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Paper • 2002.08155 • Published • 2 -
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
Paper • 2402.14658 • Published • 78 -
CodeFusion: A Pre-trained Diffusion Model for Code Generation
Paper • 2310.17680 • Published • 68 -
CodePlan: Repository-level Coding using LLMs and Planning
Paper • 2309.12499 • Published • 69
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Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 46 -
PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 56 -
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Paper • 2404.02575 • Published • 46 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 91
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The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Paper • 2210.14986 • Published • 4 -
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Paper • 2311.10702 • Published • 17 -
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 72 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 29
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VideoBooth: Diffusion-based Video Generation with Image Prompts
Paper • 2312.00777 • Published • 19 -
MotionCtrl: A Unified and Flexible Motion Controller for Video Generation
Paper • 2312.03641 • Published • 19 -
GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation
Paper • 2312.04557 • Published • 12 -
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
Paper • 2312.04433 • Published • 9