Data annotation and synthesis generally refers to the labeling or generating of raw data with
relevant information, which could be used for improving the efficacy of machine learning …

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial
factors to achieve success is aligning the LLM's output with human preferences. This …

Reinforcement Learning with Human Feedback (RLHF) is the key to the success of large
language models (LLMs) in recent years. In this work, we first introduce the concepts of …

Domain-specific Named Entity Recognition (NER), whose goal is to recognize domain-
specific entities and their categories, provides an important support for constructing domain …

Fine-tuning large language models (LLMs) for specific tasks requires high-quality, diverse
training data relevant to the task. Recent research has leveraged LLMs to synthesize …

Preference optimization is a standard approach to fine-tuning large language models to
align with human preferences. The quantity, diversity, and representativeness of the …