Language Model Ensembling for Copyright and Privacy Risk Control
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Solution Overview
Problem
Large language models (LLMs) face challenges with data usage issues such as copyright infringement, data poisoning, and privacy concerns due to training on uncurated datasets, posing legal, ethical, and financial risks.
Innovation Solution
Implementing model ensembling by combining a large language model (LLM) trained on uncurated data with a small language model (SLM) trained on curated, verified datasets using the CP-ΔKL algorithm to generate output tokens, adjusting logits with scaling factors to ensure compliance and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs are trained on extensive web-sourced training data to improve model performance, then model capability is improved, but risks of copyright infringement, data poisoning, and privacy violations increase
Solution Approach 1:
The training data is segmented into two distinct sources: web-sourced data for improving model capability and curated benign data for ensuring safety and compliance. The model is trained separately on these segmented data sources, allowing independent optimization of capability and safety without mutual contamination
Solution Approach 2:
Curated benign datasets act as an intermediary between the harmful web-sourced data and the model training process. This intermediary data source provides a safe bridge that allows the model to learn from extensive data while being protected from copyright infringement, data poisoning, and privacy violations through carefully selected training materials
2Object-affected harmful factors
If comprehensive data curation is performed to eliminate harmful data, then safety and compliance are improved, but labor intensity and complexity increase
Solution Approach 1:
Data curation and verification are performed preliminarily during the dataset preparation phase rather than during model training or deployment. Benign datasets are pre-curated and verified before being used for training, eliminating the need for complex real-time verification systems and reducing operational complexity
Solution Approach 2:
Instead of manually curating and verifying each training example, the approach uses pre-curated benchmark datasets that have already been verified for safety and compliance. These copied, verified datasets serve as reliable training materials without requiring additional curation effort for each training run
3Productivity
If LLMs are trained on uncurated data to maintain training efficiency, then training speed is improved, but model reliability and safety decrease
Solution Approach 1:
The training process merges two data sources with complementary properties: web-sourced data provides volume and diversity for efficient training, while curated benign data provides safety and compliance. Both data sources are combined in the training objective, allowing the model to simultaneously achieve training efficiency and model safety without sacrificing either
Data Source
AI summary
Provided herein are systems, methods, and computer-readable media for improving results from machine learning models. An example method may include obtaining a user input query; generating first logits from a first language model by applying the first language model to the user input query; generating second logits from a second language model by applying the second language model to the user input query; combining the first logits and the second logits; determining probabilities associated with tokens from the combined first logits and second logits; and generating an output token based on the determined one or more probabilities.