Unsupervised Elicitation of Language Models(arxiv.org)

128 points by kordlessagain 1 year ago | 18 comments

Large language models often know when they are being evaluated(arxiv.org)

60 points by jonbaer 1 year ago | 74 comments

Training LLMs to generate text with citations via fine-grained rewards(arxiv.org)

160 points by PaulHoule 2 years ago | 26 comments

Training Language Models to Self-Correct via Reinforcement Learning(arxiv.org)

216 points by weirdcat 2 years ago | 86 comments

Training LLMs for Honesty via Confessions(arxiv.org)

56 points by arabello 10 months ago | 35 comments

Training LLMs to Reason in a Continuous Latent Space(arxiv.org)

265 points by omarsar 1 year ago | 93 comments

Train Your Own LLM from Scratch(github.com)

376 points by kristianpaul 5 months ago | 43 comments

GitHub repository by angelos-p providing code and docs for training your own large language model from scratch.

Deep Learning Is Not So Mysterious or Different(arxiv.org)

445 points by wuubuu 1 year ago | 112 comments

arXiv paper by Andrew Gordon Wilson arguing deep learning generalization phenomena like benign overfitting and double descent are explainable by soft inductive biases.

Deep learning gets the glory, deep fact checking gets ignored(rachel.fast.ai)

549 points by chmaynard 1 year ago | 134 comments

Rachel Thomas blog post on how a high-profile Nature paper using a Transformer to predict enzyme functions contained many errors that received far less attention than the original result.

AI Made Writing Code Easier. It Made Being an Engineer Harder(ivanturkovic.com)

343 points by saikatsg 7 months ago | 242 comments

Blog post by Ivan Turkovic arguing that AI tools make producing code easier while making the broader job of being a software engineer harder.

Training LLMs from ground zero as a startup(yitay.net)

360 points by swyx 2 years ago | 92 comments

Yi Tay's blog post on the experience of training large language models from scratch at a startup (Reka), covering compute, infrastructure, and lessons learned.

AI tools are spotting errors in research papers(nature.com)

555 points by kgwgk 1 year ago | 200 comments

Nature news article on a growing movement using AI and large language models to detect errors in research papers, sparked by a flawed black plastic utensils study.