Mechanistic Anomaly Detection Research Update
Interim report on ongoing work on mechanistic anomaly detection
Interim report on ongoing work on mechanistic anomaly detection
Building and evaluating an open-source pipeline for auto-interpretability
Writing up results from a recent project
Achieving even more surgical edits than LEACE without concept labels at inference time.
Writing up results from a project from Spring 2023
Trained T5 on the Pile
Setting the record straight regarding Yi-34B and Llama 2.
Announcing a new resource, the FM Dev Cheatsheet.
Achieving even more surgical edits than LEACE when we have concept labels at inference time.
Explaining a result by Sam Marks and Max Tegmark
Introduction At the third New England RLHF Hackathon, several interesting projects were showcased, each focusing on different aspects of machine learning and reinforcement learning. Participants and those interested in …
How position interpolation, NTK-aware methods, and YaRN extend the context windows of language models using RoPE.
Evaluating transparency requires precision.
ArXiv | Models | Data | Code | Blog | Sample Explorer Today we release Llemma: 7 billion and 34 billion parameter language models for mathematics. The Llemma models were initialized with Code Llama weights, then trained …
Introduction Rekindling the spirit of collaboration, the New England RLHF Hackers (NERH) hosted their second hackathon at Brown University on October 8th, 2023. Stepping up from the success of our inaugural hackathon, …