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Tree Borrows(plf.inf.ethz.ch)
557 points by zdw | 141 comments
ETH Zürich Programming Language Foundations Lab page for the PLDI 2025 paper Tree Borrows, an aliasing model for Rust by Villani, Hostert, Dreyer, and Jung.
Train Your Own LLM from Scratch(github.com)
376 points by kristianpaul | 43 comments
GitHub repository by angelos-p providing code and docs for training your own large language model from scratch.
Training LLMs from ground zero as a startup(yitay.net)
360 points by swyx | 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.
Training language models to be warm and empathetic makes them less reliable(arxiv.org)
334 points by Cynddl | 333 comments
arXiv paper finding that fine-tuning language models to be warm and empathetic raises error rates and makes them more sycophantic on safety-critical tasks.
AI tools are spotting errors in research papers(nature.com)
555 points by kgwgk | 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.
Some critical issues with the SWE-bench dataset(arxiv.org)
331 points by joshwa | 113 comments
arXiv paper introducing SWE-Bench+, an empirical analysis revealing data quality issues in the SWE-bench coding benchmark for LLMs, such as solution leakage and weak tests.












