What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions

Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff Schneider, Eduard Hovy, Roger Grosse, Eric Xing·May 22, 2024

Summary

This paper addresses the issue of attributing data value in large language models by introducing LOGRA, a scalable and efficient gradient projection method for data valuation. LOGRA reduces compute and memory costs, making it practical for valuing massive datasets like Llama-3B-8B-Instruct. The work also presents LOGIX, a user-friendly software package simplifying implementation. The study evaluates LOGRA's accuracy and efficiency, showing competitive results with significant speedup and memory savings compared to EKFAC. LOGRA is applicable to various models and datasets, addressing the need for fair compensation for data providers and promoting trust in the data valuation process. The paper also discusses related techniques, limitations, and future directions for improving data attribution in the context of large-scale AI models.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the data valuation problem, specifically focusing on enabling LLM-scale data valuation using influence functions . This problem involves determining how data should be valued based on a method that is still under development. While the concept of data valuation is not new, the approach proposed in the paper represents an initial attempt to tackle the technical challenges associated with valuing data at a large scale .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to the influence functions used in data valuation for large language models (LLMs) like GPT . The hypothesis revolves around understanding the factors that contribute to the quality of the most valuable data identified by influence functions in LLMs. It explores how training data quality and training steps impact the identification of valuable data by influence functions . The paper delves into the idea that as LLMs are trained on high-quality datasets, influence functions are more likely to capture data that aligns closely with the output of the LLM, indicating a correlation between training data quality and the data identified as most valuable by influence functions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a unified and noise-reduced data valuation framework for machine learning . It introduces a method for influence selection in active learning . Additionally, the paper discusses optimizing neural networks with kronecker-factored approximate curvature . The study also presents a model for attributing model behavior at scale, known as TRAK . Furthermore, the paper explores the concept of equitable valuation of data for machine learning through Data Shapley . Lastly, the paper delves into the estimation of training data influence by tracing gradient descent . The paper introduces a unified and noise-reduced data valuation framework for machine learning, which offers several key advantages compared to previous methods. One notable characteristic is the focus on enabling the easy conversion of users' efficient training codes into data valuation codes, without the need to write gradient computation code from scratch . This design is motivated by the observation that gradients are a by-product of the training procedure, allowing for the reuse of most training code for data valuation . Additionally, the framework supports efficient computation of dataset-level statistics such as the Hessian for accurate influence computations, which is crucial for data valuation .

Moreover, the paper discusses the optimization of neural networks with kronecker-factored approximate curvature, which contributes to enhancing the efficiency and performance of the models. This optimization technique aims to improve the training process by approximating the curvature of the loss function, leading to more effective learning . Furthermore, the study presents a model for attributing model behavior at scale, known as TRAK, which provides insights into the behavior of models in a scalable manner, offering a deeper understanding of model performance and decision-making processes .

Another significant aspect highlighted in the paper is the exploration of equitable valuation of data for machine learning through Data Shapley, which introduces a fair and transparent approach to valuing data contributions in machine learning tasks . This method ensures that each data point is appropriately valued based on its impact on model performance, promoting fairness and accuracy in data valuation processes . Additionally, the paper delves into the estimation of training data influence by tracing gradient descent, which enhances the understanding of how training data affects model learning and decision-making .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers and researchers exist in the field of data valuation and influence functions. Noteworthy researchers in this area include Yixiong Chen, Alan Yuille, Zongwei Zhou, Sang Choe, Sanket Vaibhav Mehta, Emma Strubell, Eric Xing, Pradeep Dubey, Abraham Neyman, Robert James Weber, Vitaly Feldman, Chiyuan Zhang, Raul Castro Fernandez, Leo Gao, Stella Biderman, Sid Black, and many others .

The key to the solution mentioned in the paper "What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions" involves a comprehensive understanding and application of influence functions to evaluate the value of data for machine learning models. Influence functions play a crucial role in interpreting, debugging, and attributing model behavior, thereby providing insights into the impact of individual data points on model predictions .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the effectiveness of the LOGRA method in terms of accuracy and efficiency . The experiments included two types of counterfactual evaluations to quantitatively study data valuation accuracy on small-scale setups . Additionally, the paper scaled LOGRA to Large Language Models (LLMs) and their massive training data to investigate qualitative accuracy and memory/compute efficiency . The experiments involved evaluating the accuracy in successfully identifying top valuable data through methods like the brittleness test and linear datamodeling score . The brittleness test focused on the accuracy of identifying top valuable data by removing them and retraining the model multiple times with different random seeds . The linear datamodeling score measured the mean and standard deviation of LDS obtained from distinctly trained models . The experiments also included detailed descriptions of hyperparameters and compute resources used .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the OpenWebText (OWT) dataset . The code for the software package LOGIX, which facilitates data valuation, is open source and available under the Apache 2.0 license .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need verification. The study delves into the influence functions' analysis of different models, such as Pythia-1.4B, Llama3-8B-Instruct, and GPT2-XL, to understand the quality of the most valuable data generated by these models . The analysis highlights the impact of training data quality and training steps on the model's learning process, shedding light on how these factors influence the model's performance and data valuation . Additionally, the study explores the concept of influence functions capturing similar data to the query LLM output, indicating a correlation between model training on high-quality datasets and the data captured by influence functions .

Moreover, the paper discusses the implications of artificial intelligence (AI) on human intelligence and the potential future scenarios involving AI surpassing human capabilities . It addresses the role of AI in enhancing human tasks, the concerns surrounding superintelligent machines, and the alignment of AI goals with human interests . These discussions provide a comprehensive analysis of the impact of AI on human society and the ethical considerations associated with the development of advanced AI technologies .

Overall, the experiments and results outlined in the paper offer valuable insights into the interplay between data quality, model training, and the implications of AI advancements on human intelligence. The analysis contributes to the scientific understanding of AI models, data valuation techniques, and the broader implications of AI technology on society and human well-being.


What are the contributions of this paper?

The contributions of the paper "What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions" include:

  • Providing a unified and noise-reduced data valuation framework for machine learning .
  • Introducing influence selection for active learning .
  • Exploring the optimization of neural networks with kronecker-factored approximate curvature .
  • Introducing transformerlens for data valuation without the training of a model .
  • Proposing TRAK for attributing model behavior at scale .
  • Estimating training data influence by tracing gradient descent .
  • Discussing the concept of language models as unsupervised multitask learners .
  • Exploring the limits of transfer learning with a unified text-to-text transformer .
  • Addressing the equitable valuation of data for machine learning through Data Shapley .
  • Studying large language model generalization with influence functions .
  • Unifying corroborative and contributive attributions in large language models .

What work can be continued in depth?

To delve deeper into the field of artificial intelligence (AI), one promising direction is exploring the capabilities of learning models for more sequential computations. Deep learning has shown success due to its ability to perform sequential computations more effectively than previous models . This advancement has paved the way for tasks like self-driving cars and real-time language translation, showcasing the potential for further exploration and innovation in this area . Additionally, the development of new software tools like LOGIX can facilitate the implementation of data valuation systems, enabling easier integration with existing training code and promoting compatibility within the large language model (LLM) ecosystem . By focusing on enhancing the sequential computational abilities of learning models and leveraging advanced software tools, researchers can continue to push the boundaries of AI capabilities and applications.


Introduction
Background
Emergence of large language models and data value importance
Challenges in attributing value to massive datasets
Objective
Introduce LOGRA and LOGIX: scalable data valuation methods
Address practicality for large datasets like Llama-3B-8B-Instruct
Promote fair compensation and trust in data valuation
Method
LOGRA: Scalable Gradient Projection Algorithm
Compute and Memory Efficiency
Reduction in computational and memory requirements
Optimized for large-scale models
Implementation
Description of LOGRA's core algorithm
Comparison with EKFAC in terms of efficiency
LOGIX: User-Friendly Software Package
Features and design of the software
Simplification of LOGRA implementation for practitioners
Evaluation
Accuracy and Efficiency Comparison
LOGRA vs EKFAC: Competitive results and performance gains
Application to various models and datasets
Results and Analysis
LOGRA's practical impact on data valuation
Speedup and memory savings achieved
Limitations and Discussion
Comparison with existing data attribution techniques
Addressing current challenges in large-scale AI models
Future Directions
Opportunities for improvement in data attribution
Enhancements to LOGRA and LOGIX for next-generation models
Conclusion
Summary of LOGRA and LOGIX contributions
Implications for data providers, researchers, and the AI ecosystem
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How does LOGRA address the compute and memory challenges in valuing large datasets like Llama-3B-8B-Instruct?
What is the purpose of the LOGIX software package mentioned in the paper?
What method does the paper introduce for data valuation in large language models?
How does LOGRA compare to EKFAC in terms of accuracy and efficiency, according to the study?

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions

Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff Schneider, Eduard Hovy, Roger Grosse, Eric Xing·May 22, 2024

Summary

This paper addresses the issue of attributing data value in large language models by introducing LOGRA, a scalable and efficient gradient projection method for data valuation. LOGRA reduces compute and memory costs, making it practical for valuing massive datasets like Llama-3B-8B-Instruct. The work also presents LOGIX, a user-friendly software package simplifying implementation. The study evaluates LOGRA's accuracy and efficiency, showing competitive results with significant speedup and memory savings compared to EKFAC. LOGRA is applicable to various models and datasets, addressing the need for fair compensation for data providers and promoting trust in the data valuation process. The paper also discusses related techniques, limitations, and future directions for improving data attribution in the context of large-scale AI models.
Mind map
Comparison with EKFAC in terms of efficiency
Description of LOGRA's core algorithm
Optimized for large-scale models
Reduction in computational and memory requirements
Addressing current challenges in large-scale AI models
Comparison with existing data attribution techniques
Simplification of LOGRA implementation for practitioners
Features and design of the software
Implementation
Compute and Memory Efficiency
Promote fair compensation and trust in data valuation
Address practicality for large datasets like Llama-3B-8B-Instruct
Introduce LOGRA and LOGIX: scalable data valuation methods
Challenges in attributing value to massive datasets
Emergence of large language models and data value importance
Implications for data providers, researchers, and the AI ecosystem
Summary of LOGRA and LOGIX contributions
Enhancements to LOGRA and LOGIX for next-generation models
Opportunities for improvement in data attribution
Limitations and Discussion
Application to various models and datasets
LOGRA vs EKFAC: Competitive results and performance gains
Accuracy and Efficiency Comparison
LOGIX: User-Friendly Software Package
LOGRA: Scalable Gradient Projection Algorithm
Objective
Background
Conclusion
Future Directions
Results and Analysis
Evaluation
Method
Introduction
Outline
Introduction
Background
Emergence of large language models and data value importance
Challenges in attributing value to massive datasets
Objective
Introduce LOGRA and LOGIX: scalable data valuation methods
Address practicality for large datasets like Llama-3B-8B-Instruct
Promote fair compensation and trust in data valuation
Method
LOGRA: Scalable Gradient Projection Algorithm
Compute and Memory Efficiency
Reduction in computational and memory requirements
Optimized for large-scale models
Implementation
Description of LOGRA's core algorithm
Comparison with EKFAC in terms of efficiency
LOGIX: User-Friendly Software Package
Features and design of the software
Simplification of LOGRA implementation for practitioners
Evaluation
Accuracy and Efficiency Comparison
LOGRA vs EKFAC: Competitive results and performance gains
Application to various models and datasets
Results and Analysis
LOGRA's practical impact on data valuation
Speedup and memory savings achieved
Limitations and Discussion
Comparison with existing data attribution techniques
Addressing current challenges in large-scale AI models
Future Directions
Opportunities for improvement in data attribution
Enhancements to LOGRA and LOGIX for next-generation models
Conclusion
Summary of LOGRA and LOGIX contributions
Implications for data providers, researchers, and the AI ecosystem

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the data valuation problem, specifically focusing on enabling LLM-scale data valuation using influence functions . This problem involves determining how data should be valued based on a method that is still under development. While the concept of data valuation is not new, the approach proposed in the paper represents an initial attempt to tackle the technical challenges associated with valuing data at a large scale .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to the influence functions used in data valuation for large language models (LLMs) like GPT . The hypothesis revolves around understanding the factors that contribute to the quality of the most valuable data identified by influence functions in LLMs. It explores how training data quality and training steps impact the identification of valuable data by influence functions . The paper delves into the idea that as LLMs are trained on high-quality datasets, influence functions are more likely to capture data that aligns closely with the output of the LLM, indicating a correlation between training data quality and the data identified as most valuable by influence functions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a unified and noise-reduced data valuation framework for machine learning . It introduces a method for influence selection in active learning . Additionally, the paper discusses optimizing neural networks with kronecker-factored approximate curvature . The study also presents a model for attributing model behavior at scale, known as TRAK . Furthermore, the paper explores the concept of equitable valuation of data for machine learning through Data Shapley . Lastly, the paper delves into the estimation of training data influence by tracing gradient descent . The paper introduces a unified and noise-reduced data valuation framework for machine learning, which offers several key advantages compared to previous methods. One notable characteristic is the focus on enabling the easy conversion of users' efficient training codes into data valuation codes, without the need to write gradient computation code from scratch . This design is motivated by the observation that gradients are a by-product of the training procedure, allowing for the reuse of most training code for data valuation . Additionally, the framework supports efficient computation of dataset-level statistics such as the Hessian for accurate influence computations, which is crucial for data valuation .

Moreover, the paper discusses the optimization of neural networks with kronecker-factored approximate curvature, which contributes to enhancing the efficiency and performance of the models. This optimization technique aims to improve the training process by approximating the curvature of the loss function, leading to more effective learning . Furthermore, the study presents a model for attributing model behavior at scale, known as TRAK, which provides insights into the behavior of models in a scalable manner, offering a deeper understanding of model performance and decision-making processes .

Another significant aspect highlighted in the paper is the exploration of equitable valuation of data for machine learning through Data Shapley, which introduces a fair and transparent approach to valuing data contributions in machine learning tasks . This method ensures that each data point is appropriately valued based on its impact on model performance, promoting fairness and accuracy in data valuation processes . Additionally, the paper delves into the estimation of training data influence by tracing gradient descent, which enhances the understanding of how training data affects model learning and decision-making .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers and researchers exist in the field of data valuation and influence functions. Noteworthy researchers in this area include Yixiong Chen, Alan Yuille, Zongwei Zhou, Sang Choe, Sanket Vaibhav Mehta, Emma Strubell, Eric Xing, Pradeep Dubey, Abraham Neyman, Robert James Weber, Vitaly Feldman, Chiyuan Zhang, Raul Castro Fernandez, Leo Gao, Stella Biderman, Sid Black, and many others .

The key to the solution mentioned in the paper "What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions" involves a comprehensive understanding and application of influence functions to evaluate the value of data for machine learning models. Influence functions play a crucial role in interpreting, debugging, and attributing model behavior, thereby providing insights into the impact of individual data points on model predictions .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the effectiveness of the LOGRA method in terms of accuracy and efficiency . The experiments included two types of counterfactual evaluations to quantitatively study data valuation accuracy on small-scale setups . Additionally, the paper scaled LOGRA to Large Language Models (LLMs) and their massive training data to investigate qualitative accuracy and memory/compute efficiency . The experiments involved evaluating the accuracy in successfully identifying top valuable data through methods like the brittleness test and linear datamodeling score . The brittleness test focused on the accuracy of identifying top valuable data by removing them and retraining the model multiple times with different random seeds . The linear datamodeling score measured the mean and standard deviation of LDS obtained from distinctly trained models . The experiments also included detailed descriptions of hyperparameters and compute resources used .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the OpenWebText (OWT) dataset . The code for the software package LOGIX, which facilitates data valuation, is open source and available under the Apache 2.0 license .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need verification. The study delves into the influence functions' analysis of different models, such as Pythia-1.4B, Llama3-8B-Instruct, and GPT2-XL, to understand the quality of the most valuable data generated by these models . The analysis highlights the impact of training data quality and training steps on the model's learning process, shedding light on how these factors influence the model's performance and data valuation . Additionally, the study explores the concept of influence functions capturing similar data to the query LLM output, indicating a correlation between model training on high-quality datasets and the data captured by influence functions .

Moreover, the paper discusses the implications of artificial intelligence (AI) on human intelligence and the potential future scenarios involving AI surpassing human capabilities . It addresses the role of AI in enhancing human tasks, the concerns surrounding superintelligent machines, and the alignment of AI goals with human interests . These discussions provide a comprehensive analysis of the impact of AI on human society and the ethical considerations associated with the development of advanced AI technologies .

Overall, the experiments and results outlined in the paper offer valuable insights into the interplay between data quality, model training, and the implications of AI advancements on human intelligence. The analysis contributes to the scientific understanding of AI models, data valuation techniques, and the broader implications of AI technology on society and human well-being.


What are the contributions of this paper?

The contributions of the paper "What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions" include:

  • Providing a unified and noise-reduced data valuation framework for machine learning .
  • Introducing influence selection for active learning .
  • Exploring the optimization of neural networks with kronecker-factored approximate curvature .
  • Introducing transformerlens for data valuation without the training of a model .
  • Proposing TRAK for attributing model behavior at scale .
  • Estimating training data influence by tracing gradient descent .
  • Discussing the concept of language models as unsupervised multitask learners .
  • Exploring the limits of transfer learning with a unified text-to-text transformer .
  • Addressing the equitable valuation of data for machine learning through Data Shapley .
  • Studying large language model generalization with influence functions .
  • Unifying corroborative and contributive attributions in large language models .

What work can be continued in depth?

To delve deeper into the field of artificial intelligence (AI), one promising direction is exploring the capabilities of learning models for more sequential computations. Deep learning has shown success due to its ability to perform sequential computations more effectively than previous models . This advancement has paved the way for tasks like self-driving cars and real-time language translation, showcasing the potential for further exploration and innovation in this area . Additionally, the development of new software tools like LOGIX can facilitate the implementation of data valuation systems, enabling easier integration with existing training code and promoting compatibility within the large language model (LLM) ecosystem . By focusing on enhancing the sequential computational abilities of learning models and leveraging advanced software tools, researchers can continue to push the boundaries of AI capabilities and applications.

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