EasyDataset: End-to-End Guide for Generating QA Datasets for LLM Fine‑Tuning

This article walks through the complete workflow of using EasyDataset to create high‑quality question‑answer pairs for supervised fine‑tuning, covering question generation (single and batch), three generation algorithms, answer generation (including chain‑of‑thought and multi‑turn dialogue), a hybrid quality‑assessment pipeline, and export to Alpaca or ShareGPT formats.

Alpaca formatData QualityEasyDataset

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EasyDataset: End-to-End Guide for Generating QA Datasets for LLM Fine‑Tuning

Architecture and Beyond

Feb 22, 2025 · Artificial Intelligence

Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models

The article explains how the inherent knowledge‑staleness, hallucination, lack of private data, non‑traceable output, limited long‑text handling, and data‑security concerns of large language models can be mitigated by Retrieval‑Augmented Generation, which combines external retrieval, augmentation, and generation to provide up‑to‑date, reliable, and secure AI responses.

AIKnowledge augmentationLLM

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Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models

DataFunTalk

Jun 30, 2022 · Artificial Intelligence

OBERT: A Billion‑Parameter Pretrained Language Model for Large‑Scale NLP Applications

The OPPO XiaoBu team introduced OBERT, a series of 100M‑, 300M‑, and 1B‑parameter pretrained language models that leverage massive TB‑scale corpora, multi‑granular masking, retrieval‑augmented training, and distributed acceleration to achieve state‑of‑the‑art results on CLUE and KgCLUE benchmarks while enabling efficient industrial deployment.

Fine-tuningKnowledge augmentationNLP

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OBERT: A Billion‑Parameter Pretrained Language Model for Large‑Scale NLP Applications