The content discusses the challenges faced by Large Language Models (LLMs) in handling long-tail knowledge in Question Answering tasks. It introduces an automatic approach to generate specialized QA datasets for tail entities using Wikidata knowledge graphs. The study evaluates the performance of LLMs, specifically GPT3, on newly generated long-tail QA datasets and explores strategies to enhance their performance with external resources like Wikipedia and Wikidata.
The authors emphasize the importance of diverse QA datasets for testing the robustness of current QA models and present insights into filtering noisy questions, question granularity, difficulty control, and prompt engineering. They also discuss the significance of leveraging external resources to improve LLM performance on long-tail knowledge. The study aims to stimulate further research in automatic QA dataset generation and addressing long-tail knowledge challenges in open-domain QA tasks.
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by Rohan Kumar,... às arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01382.pdfPerguntas Mais Profundas