DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models (2310.00902v3)

Abstract: Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influence function is a principled and popular data attribution method, but its computational cost often makes it challenging to use. This issue becomes more pronounced in the setting of LLMs and text-to-image models. In this work, we propose DataInf, an efficient influence approximation method that is practical for large-scale generative AI models. Leveraging an easy-to-compute closed-form expression, DataInf outperforms existing influence computation algorithms in terms of computational and memory efficiency. Our theoretical analysis shows that DataInf is particularly well-suited for parameter-efficient fine-tuning techniques such as LoRA. Through systematic empirical evaluations, we show that DataInf accurately approximates influence scores and is orders of magnitude faster than existing methods. In applications to RoBERTa-large, Llama-2-13B-chat, and stable-diffusion-v1.5 models, DataInf effectively identifies the most influential fine-tuning examples better than other approximate influence scores. Moreover, it can help to identify which data points are mislabeled.

• The paper introduces a novel closed-form approximation that efficiently estimates the influence of individual data points in LoRA-tuned LLMs and diffusion models, reducing computational costs.
• It demonstrates orders-of-magnitude speedup and lower memory usage compared to traditional influence functions, validated across models like RoBERTa-large and Llama-2.
• The method’s bounded approximation error and strong empirical results pave the way for robust AI interpretability and improved data-centric model evaluation.

An Overview of "DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models"

The paper introduces a novel framework, DataInf, which addresses the computational challenges associated with estimating data influence in large scale generative AI models, such as LLMs and diffusion models. Understanding data influence is crucial for model interpretation, quantifying the effect of individual data points on model outputs, and improving AI transparency. Traditional methods like influence functions, though effective, are computationally intensive, especially in the context of large models fine-tuned using techniques like Low-Rank Adaptation (LoRA).

Contributions

1. DataInf Methodology:
   • DataInf proposes an innovative approach to influence approximation based on a closed-form expression, which avoids the costly operations typical in existing methods, such as iterative computations or multiple eigenvalue decompositions.
   • It leverages a mathematical reordering approximation that swaps the order of matrix inversion and averages, allowing the influence to be calculated with significantly reduced computational overhead.
2. Computational Efficiency and Applicability:
   • DataInf demonstrates superior computational performance, achieving orders of magnitude speedup compared to previous methods like LiSSA, with added efficiency in memory usage.
   • The paper showcases the method's efficiency with experimental validation on models including RoBERTa-large, Llama-2-13B-chat, and Stable-Diffusion-v1.5.
3. Theoretical Justifications:
   • The authors present a comprehensive theoretical analysis, demonstrating that DataInf's approximation error is bounded, especially in parameter-efficient fine-tuning scenarios. This provides assurances regarding the stability and reliability of its estimates.
4. Empirical Validation:
   • Extensive experiments validate DataInf's practical efficacy across three core tasks: influence approximation accuracy, mislabeled data detection, and influential data identification. Results show DataInf achieving higher correlation with exact influences and better mislabeled data detection capabilities than its peers.

Implications and Future Directions

The research holds substantial practical importance given the increasing reliance on large generative models. By facilitating the efficient computation of data influence, DataInf enhances the interpretability of these models, contributing to more transparent and robust AI systems. This development could significantly impact data-centric model evaluations and refinements, helping identify and rectify data biases, errors, and quality issues more promptly.

Theoretically, the paper opens avenues for further research on influence approximation methodologies that could be generalized beyond the specific confines of LLMs and diffusion models. Future work could explore the adaptation of DataInf-like approximations to other machine learning paradigms or paper its applications in real-time data stream analysis environments.

In conclusion, DataInf represents a vital step toward scalable and efficient data influence estimation. This framework not only propels research in understanding how individual data points impact large models but also sets a precedent for future developments in AI interpretability tools, bridging the gap between computational feasibility and accuracy.