抖文 - Technology Enthusiast Weekly (Issue 390): Large Language Models Are Useless Without Training Data 的评论
type: channel
link: https://douwen.me/archives/638/
description: Technology Enthusiasts Weekly (Issue 390): Without Corpus, Large Models Are IdiotsThis newsletter...
items: Lisa_Skeptical
items: RyanB_Notes
items: TechNewbie88
items: Dr. Patricia Wu
items: Jordan_K
items: Marcus_2024
items: Sarah_Dev
items: Alex_Chen

Lisa_Skeptical
type: item
date: 2026-05-13T17:03:33+08:00
description: Bold claim but I'm not entirely convinced. Haven't we seen fine-tuned smaller models punch way above their weight? The relationship between data quality and model size seems more nuanced than the title suggests, no?

RyanB_Notes
type: item
date: 2026-05-13T10:33:53+08:00
description: Adding to this: the open source community's biggest contribution might not be the models themselves, but collaborative datasets. Seeing more initiatives like Common Crawl makes me optimistic.

TechNewbie88
type: item
date: 2026-05-13T20:56:15+08:00
description: Can someone explain what "corpus" means? Is it just the training data or is there more to it? Asking for a friend who's learning about AI.

Dr. Patricia Wu
type: item
date: 2026-05-13T17:48:47+08:00
description: Excellent framing. In my research group, we've found that curating training datasets takes 10x longer than building model architecture. The bottleneck is data, not compute. This newsletter gets it.

Jordan_K
type: item
date: 2026-05-13T14:11:04+08:00
description: The title made me laugh out loud — "without corpus, large models are idiots" 😂 But seriously, this frames the problem perfectly. We're throwing computing power at garbage-in-garbage-out problems.

Marcus_2024
type: item
date: 2026-05-13T14:20:31+08:00
description: This is obvious though? Of course models need data. The real question is how we ensure the data quality and ethical sourcing going forward.

Sarah_Dev
type: item
date: 2026-05-13T18:22:11+08:00
description: Question: does this mean smaller, well-trained models might outperform larger ones with mediocre training data? Would love to see a deep dive on that tradeoff.

Alex_Chen
type: item
date: 2026-05-13T08:30:02+08:00
description: Absolutely spot on. I've been frustrated with LLM limitations for months, and this finally articulates why. Without quality training data, these models are just expensive pattern-matching machines. Great issue.