Deschide articolul original într-un tab nou Schimbarea acestei opțiuni se aplică imediat și reîncarcă pagina.
LECTURA
Înapoi la articole →

Adam Karvonen

adamkarvonen.github.io/feed.xml
Vizitează site-ul

Cel mai recent articol

Ultima verificare

Urmăritori 0 urmăritori
Articole: 7

  1. LLM inference is nearly deterministic. We use this to audit providers

    (Adam Karvonen)

    Adam Karvonen, Daniel Reuter, Roy Rinberg, Luke Marks, Adrià Garriga-Alonso, Keri Warr · arXiv (paper link) · Github One Minute Summary Today there’s no reliable way to verify that an inference provider is actually running the model they claim, as they mi…

  2. Frontier AI Models Still Fail at Basic Physical Tasks: A Manufacturing Case Study

    (Adam Karvonen)

    Dario Amodei, CEO of Anthropic, recently worried about a world where only 30% of jobs become automated, leading to class tensions between the automated and non-automated.

  3. Using an LLM perplexity filter to detect weight exfiltration

    (Adam Karvonen)

    A recent area of focus has been securing AI model weights.

  4. Evaluating Sparse Autoencoders with Board Games

    (Adam Karvonen)

    This blog post discusses a collaborative research paper on sparse autoencoders (SAEs), specifically focusing on SAE evaluations and a new training method we call p-annealing.

  5. An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability

    (Adam Karvonen)

    Sparse Autoencoders (SAEs) have recently become popular for interpretability of machine learning models (although sparse dictionary learning has been around since 1997).

  6. Manipulating Chess-GPT’s World Model

    (Adam Karvonen)

    Manipulating Chess-GPT’s World Model Note: This work has since been turned into a paper accepted to the Conference on Language Modeling, but the average reader will probably prefer the blog post.

  7. Chess-GPT’s Internal World Model

    (Adam Karvonen)

    A Chess-GPT Linear Emergent World Representation Introduction Note: This work has since been turned into a paper accepted to the Conference on Language Modeling, but the average reader will probably prefer the blog post.