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Practical AI Solutions for Representation Finetuning (ReFT) Methods
Parameter-efficient Finetuning (PEFT) Methods
PEFT methods offer a solution by updating only a fraction of the weights, reducing memory usage and training time.
Representation Finetuning (ReFT) Methods
ReFT methods train interventions to manipulate a small fraction of model representations, steering model behaviors to solve downstream tasks at inference time.
Low-rank Linear Subspace ReFT (LoReFT)
LoReFT intervenes on hidden representations in the linear subspace spanned by a low-rank projection matrix, demonstrating state-of-the-art performance on various benchmarks while using significantly fewer parameters than traditional PEFT methods.
ReFT methods offer more efficient and effective alternatives to weight-based PEFTs, deserving further exploration across different model families and domains.
Evaluation Practices
It’s essential to establish benchmarks that allow for fair comparisons of PEFTs and ReFTs, including compute- or time-matched hyperparameter-tuning comparisons and disallowing tuning or model selection based on the test set to mitigate overfitting and ensure real-world performance assessment.
AI Solutions for Business Evolution
Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
Select an AI Solution: Choose tools that align with your needs and provide customization.
Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
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