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Can Large Language Models Understand Context?
Paper β’ 2402.00858 β’ Published β’ 23 -
OLMo: Accelerating the Science of Language Models
Paper β’ 2402.00838 β’ Published β’ 83 -
Self-Rewarding Language Models
Paper β’ 2401.10020 β’ Published β’ 146 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper β’ 2401.17072 β’ Published β’ 25
Collections
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Collections including paper arxiv:2401.10891
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Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
Paper β’ 2312.06134 β’ Published β’ 3 -
Efficient Monotonic Multihead Attention
Paper β’ 2312.04515 β’ Published β’ 7 -
Contrastive Decoding Improves Reasoning in Large Language Models
Paper β’ 2309.09117 β’ Published β’ 39 -
Exploring Format Consistency for Instruction Tuning
Paper β’ 2307.15504 β’ Published β’ 8
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Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper β’ 2401.10891 β’ Published β’ 60 -
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Depth Anything
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LiheYoung/depth_anything_vitl14
Depth Estimation β’ Updated β’ 37.2k β’ 40 -
LiheYoung/depth_anything_vitb14
Depth Estimation β’ Updated β’ 10.6k β’ 3