Exploring How "Silly" Questions Impact Large Language Model Fine-Tuning: A Comprehensive Analysis
While incorporating "silly" questions, inspired by the Ruozhiba dataset, into large language model fine-tuning datasets can lead to slight performance improvements in specific subjects and tasks, it does not yield significant overall gains on datasets like MMLU.
VersaTune: A Novel Data Composition Framework for Enhancing Multi-Domain Capabilities of Large Language Models During Fine-Tuning
VersaTune, a novel data composition framework, enhances the multi-domain capabilities of LLMs during fine-tuning by aligning domain-specific data proportions with the pre-trained model's knowledge distribution and dynamically adjusting them based on real-time performance feedback.
DELIFT: A Data-Efficient Language Model Instruction Fine-Tuning Algorithm for Improved Performance with Reduced Datasets
DELIFT is a novel algorithm that significantly reduces data requirements for fine-tuning large language models (LLMs) without compromising performance, achieving comparable or better results than using full datasets by employing a pairwise utility metric and submodular optimization for efficient data selection across different fine-tuning stages.
低ランク適応におけるベイズ再パラメータ化を用いた、LLMのロバストかつ効率的なファインチューニング
大規模言語モデル（LLM）のファインチューニングにおける課題である、ハイパーパラメータへの感度と計算コストの高さを、低ランク適応とベイズ再パラメータ化を組み合わせたMonteCLoRAと呼ばれる新しい手法で解決する。
MonteCLoRA: A More Robust and Efficient Method for Fine-tuning Large Language Models Using Bayesian Low-Rank Adaptation
MonteCLoRA, a novel fine-tuning technique for Large Language Models (LLMs), leverages Bayesian principles and Monte Carlo estimation to enhance the robustness and efficiency of low-rank adaptation methods, addressing the sensitivity to hyperparameters often observed in traditional approaches.
인간 학습 전략 기반 의료 질의응답 시스템의 효율적인 미세 조정 평가: 모델별 일반화 가능성에 대한 고찰
인간의 학습 전략을 모방한 다양한 미세 조정 기법을 의료 질의응답 시스템에 적용한 결과, 전반적인 성능 향상을 보였지만, 모델 및 데이터셋에 따라 최적의 전략이 달라지는 제한적인 일반화 성능을 나타냈다.
Linear Chain Transformation for Efficient Fine-Tuning of Large Language Models: Enhancing Optimization Dynamics with a Sequence of Linear Transformations
LinChain, a novel fine-tuning method for large language models, enhances performance and convergence speed by introducing a sequence of linear transformations during parameter updates, offering a more expressive and efficient alternative to existing low-rank adaptation methods like LoRA.
MiLoRA: A New Method for Efficiently Fine-tuning Large Language Models Using a Mixture of Low-Rank Adaptations and Prompt-Aware Routing
MiLoRA, a novel parameter-efficient fine-tuning method, improves efficiency and performance by activating only one LoRA module per Transformer layer based on the input prompt, reducing computational overhead during inference.
Controlled Low-Rank Adaptation with Subspace Regularization for Mitigating Catastrophic Forgetting in Large Language Models
CLoRA, a novel parameter-efficient fine-tuning method for large language models, effectively mitigates catastrophic forgetting by constraining the direction of parameter updates within a low-rank subspace, achieving superior performance in both in-domain and out-domain evaluations.
The Ultimate Guide to Fine-Tuning Large Language Models: A Technical Report
This technical report provides a comprehensive overview of fine-tuning Large Language Models (LLMs), covering its evolution, methodologies, best practices, challenges, and future directions.
