Platypus: Quick, Cheap, and Powerful Refinement of LLMs
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Copyright Continuum Labs - 2023
Last updated
This March 2024 demonstrates the potential performance of base Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) on a curated dataset named Open-Platypus, focusing on the domain of LLMs in customer support settings.
The authors provide a context for their work against the backdrop of significant advancements in LLMs, noting the development of models like PaLM, GPT-3, and LLaMa, which emphasise computational efficiency and the movement towards open-source models like BLOOM and Falcon.
The paper discusses various strategies to improve LLM performance, including knowledge distillation, instruction tuning, and the Mixture of Experts approach.
These methods aim to enhance the models' efficiency and adaptability across various domains. The authors specifically employ the LoRA methodology, noting its effectiveness in their workflow and its potential for future cost and time reductions in training.
Key contributions of the paper include:
Open-Platypus Dataset
Open-Platypus is a curated dataset that the team created by selecting a subset from other open datasets.
It integrates 11 open-source datasets, predominantly consisting of human-designed questions, enabling robust performance with minimal fine-tuning time and cost.
The high-quality nature of Open-Platypus has allowed for strong performance and efficiency, demonstrating the importance of targeted and specific datasets in training sophisticated models. The dataset is also released to the public, fostering collaborative improvement.
Dataset Optimisation
The authors describe their process of similarity exclusion to streamline the dataset by reducing redundancy and a training data filtering process to avoid contamination, ensuring the dataset's integrity and relevance.
Fine-tuning and Merging Process
They detail their approach to selecting and merging specialised fine-tuned LoRA modules, highlighting the effectiveness of this method in imparting specific domain knowledge while maintaining the benefits of instruction tuning.
This work aims to advance the field by providing an efficient way to enhance LLMs for specific tasks, particularly in customer support, emphasizing the potential of domain-specific datasets and merging techniques to improve model performance while reducing training time and costs.
The paper outlines a detailed process for curating the Open-Platypus dataset, aimed at enhancing the performance of Large Language Models (LLMs), particularly focusing on the STEM domain.
Here's a breakdown of the data curation process, demystifying any jargon and explaining technical concepts:
Data Selection Criteria
The curation process was influenced by several theoretical frameworks and empirical findings suggesting that with minimal yet targeted training data, significant alignment of model outputs can be achieved. The dataset aimed to provide depth in specific areas, ensuring diversity in input prompts while maintaining a manageable size.
Open-Platypus Dataset Composition
This dataset is an aggregation of 11 open-source datasets, predominantly comprising human-generated questions, with about 10% contributed by an LLM. The focus is on STEM and logic, selecting datasets that offer questions in these domains or filtering broader datasets for relevant content.
Instruction Tuning
To enhance the dataset's effectiveness, an instruction-tuning format was employed where each data point includes an instruction, input, and output. This format is particularly useful for creating structured and consistent training material for the LLM.
De-duplication and Similarity Removal
To prevent the model from simply memorizing answers, a de-duplication process was implemented. This involved removing exact duplicates and questions with a high degree of similarity (measured by cosine similarity) to others in the dataset. This step ensures that the training data encourages the model to learn underlying patterns and logic rather than memorizing specific answers.
Contamination Check
A critical part of the curation process involved ensuring that the training data did not contain any questions from benchmark test sets. This prevents the model from giving the illusion of high performance by simply recalling answers to known questions.
Fine-tuning and Merging Process
The paper also details the use of Low Rank Approximation (LoRA) for fine-tuning the models, which is a technique that adjusts a small set of parameters in the model, making the training process more efficient and cost-effective. The fine-tuning process was carefully managed to ensure that the models improved in the target domains without requiring extensive computational resources.
The meticulous curation process of Open-Platypus aims to ensure that the fine-tuned LLMs are not only effective in their domain-specific tasks but also efficient in terms of training requirements, thereby addressing a critical aspect of AI model development.
Performance Overview
The Platypus2-70B instruct variant achieved the top position on the Hugging Face Open LLM Leader board with an impressive average score of 73.13, showcasing its superior performance among other models. The Stable-Platypus2-13B model was highlighted as the leading 13 billion parameter model with an average score of 63.96.
Model Merging and Fine-tuning
The study explored the effects of merging different models (broad and niche) and the benefits of fine-tuning using the Open-Platypus dataset. The results showed that the fine-tuned models outperformed the base models, particularly in the ARC and TruthfulQA benchmarks, demonstrating the effectiveness of the merging and fine-tuning strategy.
Impact on Various Benchmarks
The fine-tuned models showed varied performance across different benchmark tests. For example, the Camel-Platypus2-70B model significantly improved in the ARC-Challenge, whereas the Dolphin-Platypus2-70B merge did not surpass the performance of the base and adapter models. This indicates that the merging process's success can vary based on the models and datasets involved.
Domain-Specific Performance
The effectiveness of the fine-tuned models was domain-specific. For instance, in the machine learning domain, the Camel-Platypus2-70B model showed a remarkable improvement, suggesting that the choice of model for merging is crucial depending on the domain or task at hand.
Notable Improvements and Declines
The analysis highlighted significant improvements and declines in different domains. For example, the Camel-Platypus2-70B model excelled in the ARC-Challenge, while several models showed notable declines in the college physics test, indicating potential compatibility issues or limitations in certain domains.
Insights into Merging Strategy
The results provided insights into the merging strategy's complexity, showing that not all merges lead to superior models. The variability in performance across different benchmarks suggests that careful consideration is required when selecting models for merging, especially when targeting specific domains or tasks.
Overall, the results emphasize the potential of fine-tuning and merging strategies to enhance LLMs' performance, demonstrating significant improvements in specific domains while also highlighting the importance of domain-specific evaluations and the complexities involved in the model merging process.
This paper discusses the enhancement of Large Language Models (LLMs) through fine-tuning using the Open-Platypus dataset and explores the potential benefits of merging smaller, efficient models with the precision of individual adapters.
It highlights the success of these fine-tuned models in specific tasks and suggests that future work could explore integrating various datasets and methodologies like QLoRA to improve model performance.
The paper acknowledges the limitations of the Platypus model, such as its static knowledge base, potential bias, and its primary focus on English-language data.
It stresses the importance of responsible use and the need for further safety testing before deploying the model in applications. The paper also notes the significance of ensuring no contamination between training and benchmark test sets to maintain the integrity of the model's performance. Lastly, it acknowledges the contributions of Hugging Face and Meta AI in supporting the development and evaluation of LLMs.
