Metadata Might Make Language Models Better
Paper
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2211.10086
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Published
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4
Fine-Tuning
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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 • 122 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 14 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 64 -
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning
Paper • 2308.11148 • Published • 2 -
An Unsupervised Method for Estimating Class Separability of Datasets with Application to LLMs Fine-Tuning
Paper • 2305.15016 • Published • 5 -
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper • 2402.09353 • Published • 26 -
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
Paper • 2401.11649 • Published • 3 -
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Paper • 2402.17193 • Published • 23 -
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Model
Paper • 2402.17412 • Published • 21 -
Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 46 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 62 -
Scaling Laws of RoPE-based Extrapolation
Paper • 2310.05209 • Published • 6 -
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper • 2310.09263 • Published • 39 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 62 -
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 78