What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions (2405.13954v1)

Abstract: LLMs are trained on a vast amount of human-written data, but data providers often remain uncredited. In response to this issue, data valuation (or data attribution), which quantifies the contribution or value of each data to the model output, has been discussed as a potential solution. Nevertheless, applying existing data valuation methods to recent LLMs and their vast training datasets has been largely limited by prohibitive compute and memory costs. In this work, we focus on influence functions, a popular gradient-based data valuation method, and significantly improve its scalability with an efficient gradient projection strategy called LoGra that leverages the gradient structure in backpropagation. We then provide a theoretical motivation of gradient projection approaches to influence functions to promote trust in the data valuation process. Lastly, we lower the barrier to implementing data valuation systems by introducing LogIX, a software package that can transform existing training code into data valuation code with minimal effort. In our data valuation experiments, LoGra achieves competitive accuracy against more expensive baselines while showing up to 6,500x improvement in throughput and 5x reduction in GPU memory usage when applied to Llama3-8B-Instruct and the 1B-token dataset.

• The paper presents LoGra, a novel algorithm that reduces space and time complexity for influence functions from O(nk) to O(√nk), enabling efficient data valuation for LLMs.
• It provides a theoretical grounding of gradient projection as a spectral sparsification mechanism, ensuring critical gradient components are preserved.
• The work also introduces Logix, a practical software tool that integrates data valuation into LLM training workflows, demonstrating up to 6,500× throughput gains and reduced GPU memory usage.

An Insightful Overview of "What is Your Data Worth to GPT?\LLM-Scale Data Valuation with Influence Functions"

The paper "What is Your Data Worth to GPT?\LLM-Scale Data Valuation with Influence Functions" primarily addresses the growing need to credit data providers whose contributions are pivotal in training LLMs. The authors introduce several significant advancements to the existing methods of data valuation, focusing on scaling influence functions for LLMs.

Core Contributions

Efficient Gradient Projection with LoGra

A notable enhancement proposed in this paper is the development of the LoGra algorithm. LoGra addresses the critical challenge of computing and memory costs associated with traditional influence functions. By leveraging the inherent gradient structure in backpropagation, LoGra employs a low-rank gradient projection technique. This approach reduces the space and time complexity from $O(nk)$ to $O(\sqrt{nk})$, where $n$ is the model dimension and $k$ is the projection dimension. Furthermore, LoGra achieves this efficiency by directly computing projected gradients without having to materialize full gradients, significantly lowering GPU memory usage and boosting throughput.

The efficiency of LoGra is demonstrated through rigorous empirical evaluation. When applied to Llama3-8B-Instruct and a 1B-token dataset, LoGra shows up to 6,500 times improvement in throughput and a fivefold reduction in GPU memory usage compared to EKFAC influence, the current state-of-the-art at this scale.

Theoretical Grounding and Gradient Sparsification

To promote trust in the data valuation process, the paper also provides a theoretical motivation for gradient projection in influence functions. By interpreting the damping term in influence functions as a spectral gradient sparsification mechanism, the authors justify the emphasis on larger gradient components, ensuring that important aspects of the gradients are preserved during projection. This theoretical insight is further solidified through a specialized PCA initialization scheme for LoGra.

Practical Implementation with Logix

In addition to the algorithmic innovation, the paper introduces Logix, a software package designed to ease the integration of data valuation into existing training workflows. Logix utilizes PyTorch hooks to intercept gradient computations and enables the calculation of various statistics required for data valuation. Its compatibility with prevalent tools in the LLM ecosystem, such as DeepSpeed and HF Transformers, and its ability to efficiently handle data IO through memory-mapped files, render it particularly useful for large-scale applications.

Experimental Validation

The efficacy of LoGra and influence functions is validated through both quantitative and qualitative experiments.

Quantitative Evaluation: Using benchmarks like FMNIST with MLP, CIFAR-10 with ResNet-9, and WikiText with GPT2, the paper conducts counterfactual evaluations, including brittleness tests and linear datamodeling scores (LDS). These experiments highlight LoGra's competitive accuracy, particularly in identifying top valuable data and general valuation accuracy.

Qualitative Evaluation: When scaling to billion-scale models and datasets, the paper assesses the qualitative similarities between LLM outputs and the most valuable data identified by LoGra. For models like GPT2-XL and Llama3-8B-Instruct, the findings reveal a notable congruence in semantics, style, and token overlaps, bolstering the credibility of the data valuation process.

Practical and Theoretical Implications

The advancements presented in the paper hold substantial implications both in practice and theory. Practically, the ability to efficiently and accurately value data at the LLM scale opens avenues for transparent and fair data attribution, potentially addressing the legal and ethical concerns surrounding the use of uncredited data in LLM training. Theoretically, the insights into gradient projection and sparsification contribute to the broader understanding of influence functions and their applicability to large-scale neural network training.

Future Directions

While the proposed methods demonstrate significant improvements, the paper also acknowledges certain limitations. For instance, the challenges of dealing with outlier data points and the need for more extensive system optimizations (e.g., incorporating a high-performance vector database) are areas identified for future research. Moreover, exploring alternative gradient compression strategies could further enhance computational efficiency.

Conclusion

In conclusion, "What is Your Data Worth to GPT?\LLM-Scale Data Valuation with Influence Functions" presents a comprehensive solution to scale data valuation methodologies to modern LLMs. The introduction of LoGra, grounded in theoretical robustness and supported by practical implementation via Logix, marks a significant step forward. This research not only enhances the scalability and accuracy of data valuation techniques but also underscores the importance of attributing credit to data providers, paving the way for more transparent and equitable AI applications.