Training Data for Open Source LLM Classification

Types of Training Data

General Language Corpora

Large-scale text datasets to build foundational language understanding:

• Wikipedia articles
• Web crawl data
• Books and literature
• News articles

Example: LLaMA models are trained on a mixture of 7 publicly available datasets, comprising 1.4T tokens (Jiao et al., 2023)

Task-Specific Datasets

Curated datasets for specific classification tasks:

• Sentiment analysis corpora
• Topic classification datasets
• Named entity recognition data
• Question-answering pairs

Example: The COIG dataset for Chinese instruction-following tasks (Jiao et al., 2023)

Instruction-Tuning Data

Datasets designed to improve model's ability to follow instructions:

• Human-written instructions and responses
• Multi-turn conversations
• Task-specific prompts and completions

Example: The OpenAssistant dataset, a crowd-sourced conversational dataset for RLHF training (Han et al., 2023)

Data Quality and Preprocessing

Data Cleaning

• Removing irrelevant or low-quality content
• Handling special characters and formatting
• Normalizing text (e.g., lowercase, removing extra whitespace)
• Addressing potential biases or inappropriate content

Note: Careful preprocessing is crucial to avoid introducing biases or errors in the training data.

Data Augmentation

• Techniques to increase dataset size and diversity:
  • Synonym replacement
  • Back-translation
  • Text generation using existing LLMs

Example: The Panda LLM project uses up-sampling techniques on the COIG dataset to improve performance (Jiao et al., 2023)

Domain-Specific Considerations

Medical Domain

• Specialized medical datasets:
  • Medical exam questions (e.g., USMLE)
  • PubMed articles and abstracts
  • Electronic health records (anonymized)
  • Medical forum conversations

Example: MedAlpaca uses various medical datasets for training, including MedQA and PubMed Causal Benchmark (Han et al., 2023)

Multilingual and Cross-lingual Data

• Datasets in multiple languages
• Parallel corpora for translation tasks
• Code-switched data for multilingual models

Example: The Panda LLM project incorporates Chinese-specific datasets to enhance performance on Chinese language tasks (Jiao et al., 2023)

Ethical Considerations and Bias Mitigation

Addressing Hate, Abuse, and Profanity (HAP)

• Importance of detecting and mitigating HAP content in training data
• Implementing HAP detectors to create civil and unbiased LLMs
• Balancing between realistic language representation and ethical concerns

Example: Research on efficient models for detecting Hate, Abuse, and Profanity in multiple languages (Tillmann et al., 2024)

Fairness and Representation

• Ensuring diverse representation in training data
• Avoiding reinforcement of stereotypes or biases
• Considering cultural and regional differences in language use

Note: Careful curation and analysis of training data is essential to promote fairness and reduce biases in the resulting models.

Data Generation Techniques

Synthetic Data Generation

• Using existing LLMs to generate labeled training data
• Zero-shot learning via dataset generation
• Prompting techniques for diverse and high-quality synthetic data

Example: The Fabricator toolkit for generating labeled training data using teacher LLMs (Golde et al., 2023)

Human-in-the-Loop Data Creation

• Combining automated generation with human verification
• Iterative refinement of generated data
• Ensuring data quality and relevance to the target task

Note: This approach can help balance the efficiency of automated generation with the accuracy of human annotation.

Training Strategies

Fine-tuning Approaches

• Full fine-tuning of all model parameters
• Parameter-efficient fine-tuning techniques (e.g., LoRA)
• Instruction tuning for improved task performance

Example: MedAlpaca explores various fine-tuning approaches, including full fine-tuning and LoRA (Han et al., 2023)

Data Sampling and Balancing

• Techniques to handle class imbalance in classification tasks
• Curriculum learning approaches
• Dynamic data sampling during training

Note: Proper data sampling and balancing can significantly impact model performance and generalization.

Evaluation and Benchmarking

Task-Specific Evaluation Metrics

• Classification accuracy, F1 score, precision, recall
• Domain-specific metrics (e.g., BLEU for translation)
• Human evaluation for subjective tasks

Example: MedAlpaca uses USMLE exam performance as an evaluation metric for medical knowledge (Han et al., 2023)

Cross-Domain Generalization

• Evaluating model performance on out-of-domain tasks
• Zero-shot and few-shot learning capabilities
• Assessing transfer learning potential

Note: Assessing generalization helps understand the model's robustness and applicability to diverse classification tasks.