Training LLM models traditionally require extensive human intervention, which is both time-consuming and costly. The cost of human data labelling, for instance, alone can be substantial.
According to Google Cloud, the price for labelling tasks can range from $0.05 to $0.30 per label, and large-scale projects often require millions of labels, leading to costs that can easily reach hundreds of thousands to millions of dollars.
So, here are methods that can be used to reduce human intervention and the overall cost of training LLMs.
Plan like a Graph (PLaG)
PLaG involves encoding graph structures into a format that LLMs can process. By representing nodes and edges as tokens, LLMs can learn to understand and manipulate graph-based data, enhancing their reasoning capabilities and problem-solving skills.
Graph-based learning enables LLMs to handle complex, structured data more effectively, making them suitable for applications like knowledge graphs, molecule discovery, and network analysis.
Self-Rewarding Language Models from Meta
Meta recently published a paper explaining how Self-Rewarding Language Models (SRLMs) can be used to train LLMs without human intervention. SRLMs use LLM-as-a-Judge prompting to generate their own rewards during training. This iterative process allows the model to improve its instruction-following capabilities and reward-modelling abilities without human feedback.
This approach reduces dependency on human-generated data and feedback, enabling continuous self-improvement and potentially surpassing human performance limitations.
Nice implementation of Self Rewarding Language Models from Yuan et al., 2024, utilizing LLM-as-a-Judge to allow a model to self-improve.
Integrates Low-Rank Adaptation optimizing adaptability without full tuning.
Includes:
Automated Iteration Cycles: Ensures both training and… pic.twitter.com/K5T3R5Uzwp— Rohan Paul (@rohanpaul_ai) May 12, 2024
Autonomous Learning for LLMs
Autonomous Learning allows LLMs to learn independently by interacting with text data, similar to how humans read and comprehend literature. The model identifies and reinforces its knowledge gaps through a self-sufficient learning loop.
Autonomous learning enhances the efficiency and effectiveness of LLM training by eliminating the need for annotated data and human supervision, paving the way for more advanced and self-reliant AI systems.
Sequential Instruction Tuning (SIT)
SIT involves fine-tuning LLMs on tasks that require solving sub-tasks sequentially. This method improves the model’s ability to follow complex, multi-step instructions and enhances its performance on downstream tasks.
SIT equips LLMs with the ability to handle intricate queries and tasks, making them more versatile and capable of performing complex operations autonomously.
Interactive Self-Reflection
Through Interactive Self-Reflection (ISR), the model generates solutions to given tasks and then reviews its own responses to identify and correct errors. This iterative self-review process allows the LLM to refine its understanding and enhance its performance autonomously.
It enables LLMs to learn from their mistakes without external feedback, fostering continuous improvement. This self-reflective capability is crucial for developing more accurate and reliable AI systems that can adapt and optimise their outputs over time.
Self-Playing Adversarial Language Game (SPAG)
In SPAG, LLMs act as both attacker and defender in a two-player adversarial game. This self-play mechanism enhances the model’s reasoning abilities by forcing it to infer and express information in a competitive scenario.
It pushes LLMs to develop advanced reasoning skills and improve their performance on a broad range of benchmarks, making them more robust and capable.
Automated Design-Data Augmentation Framework
This framework generates high-quality natural language descriptions of technical scripts, such as Verilog/EDA, to augment training data. This automated process significantly reduces the time and effort required for data preparation.
Automated data augmentation enhances the robustness and accuracy of LLMs by providing diverse and high-quality training examples, leading to better performance in specialised tasks like code generation and repair.
These innovative methods represent a significant leap forward in the autonomous training of LLMs, reducing the reliance on human intervention and enabling continuous self-improvement. As these techniques evolve, they hold the promise of creating more advanced, efficient, and capable language models.
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