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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 83 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 147 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2402.17764
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Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Paper • 2402.05140 • Published • 22 -
BitDelta: Your Fine-Tune May Only Be Worth One Bit
Paper • 2402.10193 • Published • 22 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 50 -
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
Paper • 2402.14658 • Published • 82
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Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 20 -
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Paper • 2402.10644 • Published • 81 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 24 -
Zoology: Measuring and Improving Recall in Efficient Language Models
Paper • 2312.04927 • Published • 2
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Transformers are Multi-State RNNs
Paper • 2401.06104 • Published • 37 -
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Paper • 2402.10644 • Published • 81 -
In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss
Paper • 2402.10790 • Published • 42 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 610
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Rethinking Optimization and Architecture for Tiny Language Models
Paper • 2402.02791 • Published • 13 -
More Agents Is All You Need
Paper • 2402.05120 • Published • 53 -
Scaling Laws for Forgetting When Fine-Tuning Large Language Models
Paper • 2401.05605 • Published -
Aligning Large Language Models with Counterfactual DPO
Paper • 2401.09566 • Published • 2
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Scaling Laws for Downstream Task Performance of Large Language Models
Paper • 2402.04177 • Published • 18 -
Offline Actor-Critic Reinforcement Learning Scales to Large Models
Paper • 2402.05546 • Published • 5 -
SaulLM-7B: A pioneering Large Language Model for Law
Paper • 2403.03883 • Published • 80 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 610
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Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 20 -
Transforming and Combining Rewards for Aligning Large Language Models
Paper • 2402.00742 • Published • 12 -
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Paper • 2402.03300 • Published • 108 -
Specialized Language Models with Cheap Inference from Limited Domain Data
Paper • 2402.01093 • Published • 46
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BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
Paper • 2401.17053 • Published • 32 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Paper • 2402.03300 • Published • 108 -
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
Paper • 2402.05930 • Published • 39