Data Curation and LLMs

Data Curation and LLMs #

Q1: What is the background of LLMs discussed? #

  • LLMs like ChatGPT, GPT-4, and Llama are sequence models trained to predict the next token.
  • They use unsupervised pre-training on massive internet-scale corpora and can solve various NLP tasks.

Q2: What are the applications of LLMs discussed? #

  • Zero-shot prompting
  • Few-shot prompting

Q3: What is the focus of this lecture? #

  • Using LLMs for data curation
  • Evaluating LLM output data
  • Curation for LLM pre-training and application fine-tuning

Q4: How are LLMs used for data curation? #

  • They act as powerful, flexible, and computationally inexpensive reasoning engines for text data curation.

Q5: How was PII detection handled traditionally vs with LLMs? #

  • Traditional: Custom regex rules.
  • With LLMs: Zero-shot prompting to detect a wider range of PII without needing extensive rule-writing.

Q6: How can grammar checking be improved with LLMs? #

  • Traditional: Rule-based systems like LanguageTool.
  • LLM-based: Fine-tuning LLMs on curated grammatical acceptability datasets like CoLA.

Q7: What is a major challenge in working with LLM outputs? #

  • Hallucinations, where LLMs produce confidently incorrect information.

Q8: How can we evaluate LLM outputs more reliably? #

  • Use a stronger LLM (e.g., GPT-4) to judge the outputs of weaker LLMs.

Q9: What evidence supports LLM-based evaluation? #

  • AlpaGasus fine-tuning project showed better results by curating higher-quality data points evaluated by GPT-3.5 and GPT-4.

Q10: What is the challenge of using LLMs to evaluate other LLMs? #

  • It risks circular evaluation (turtles all the way down) if the same LLMs are involved in both generation and evaluation.

Q11: What method helps with LLM uncertainty quantification? #

  • Natural Language Inference (NLI)-based techniques that check for answer contradictions.

Q12: What are the key stages of data curation for LLM pre-training? #

  • Focus on corpus quality because errors are difficult to “un-learn.”
  • Use supervised fine-tuning and reinforcement learning from human feedback.

Q13: How does data curation differ for LLM applications? #

  • Zero-shot prompting
  • Few-shot prompting
  • Retrieval-augmented generation
  • Supervised fine-tuning

Q14: Why is fine-tuning important for LLM applications? #

  • Fine-tuning provides the best task-specific performance.
  • It enables training smaller models to match large model performance through synthetic data generation.

Q15: How is synthetic data curated for LLM fine-tuning? #

  • Generate synthetic data using a powerful LLM.
  • Use uncertainty quantification to retain only high-confidence examples.
  • Train classifiers to filter out unrealistic synthetic data.

Q16: What is the future trend in data curation for LLMs? #

  • Growth of powerful multi-modal LLMs and new tools like CleanVision and GPT-4 to automate and improve data quality.

References #