Complexity of Data Annotation for LLMs vs. Conventional NLP

Initial Step in Creating Machine Translation Fine-Tuning Data

Limitations of Manual Data Generation for Fine-Tuning

Difficulty of Human Annotation for Complex Tasks

A small, unfunded research lab wants to fine-tune a language model for a highly specialized, novel task: generating legal summaries of court proceedings for a niche area of patent law. They have access to a few legal experts but have a very limited budget. If they choose to have their experts create the input-output training pairs from scratch, which statement best evaluates the primary trade-off they will face?

Diagnosing Model Performance Issues

Evaluating Data Generation Strategy for a General-Purpose LLM