Collection by
Apr 11
Apr 11
Collections
Discover the best community collections!
Collections including paper arxiv:2310.05914
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Mar 14
Mar 14
Collection by
Mar 27
Mar 27
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Metadata Might Make Language Models Better
Paper • 2211.10086 • Published • 4 -
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs
Paper • 2304.14999 • Published • 2 -
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques
Paper • 2401.02122 • Published • 2 -
Zephyr: Direct Distillation of LM Alignment
Paper • 2310.16944 • Published • 121
Collection by
Aug 17
Aug 17
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LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Paper • 2309.12307 • Published • 87 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 14 -
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Paper • 2312.15166 • Published • 56 -
Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
Paper • 2401.03462 • Published • 26
Collection by
Jul 21
Jul 21
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Towards General Text Embeddings with Multi-stage Contrastive Learning
Paper • 2308.03281 • Published • 1 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 14 -
EELBERT: Tiny Models through Dynamic Embeddings
Paper • 2310.20144 • Published • 3 -
Dynamic Word Embeddings for Evolving Semantic Discovery
Paper • 1703.00607 • Published • 1
Collection by
Jul 22
Jul 22
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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 4 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 11 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1
Collection by
Jan 23
Jan 23
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 143 -
Exponentially Faster Language Modelling
Paper • 2311.10770 • Published • 118 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 28 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 14