LLMs

Principles for growable tools

There are three critical pieces to building a tool that can grow around its users over time.

1. Design around play . Sometimes I call this design around experimentation . Using the tool for day-to-day work should involve playing and experimenting with what’s possible with the tool. Whether that’s writing small programs to

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A core research interest of mine is imagining new kinds of interfaces to text documents that are made possible by modern AI and software. I think an interesting place to look for such ideas may be interface designs for reading and writing legal documents .

Legal document-wrangling tools have a handful of properties that make it fertile ground for in... See more

Humans are bad at coming up with search queries. Humans are good at incrementally narrowing down options with a series of filters, and pointing where they want to go next. This seems obvious, but we keep building interfaces for finding information that look more like Google Search and less like a map.

All information tools have to give users some wa... See more

A new v0.4.0 release of lm-evaluation-harness is available !

New updates and features include:

• Internal refactoring


• Config-based task creation and configuration


• Easier import and sharing of externally-defined task config YAMLs


• Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource


• More advanced configuration opt

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Study finds RLHF reduces LLM creativity and output variety : A new research paper posted in /r/LocalLLaMA shows that while alignment techniques like RLHF reduce toxic and biased content, they also limit the creativity of large language models, even in contexts unrelated to safety.

What’s the best way for an end user to organize and explore millions of latent space features?

I’ve found tens of thousands of interpretable features in my experiments, and frontier labs have demonstrated results with a thousand times more features in production-scale models. No doubt, as interpretability techniques advance, we’ll see feature maps t... See more

I’ve been giving talks and speaking with engineers and non-technical audiences about interpretability since 2022, and I still struggle to explain exactly what a “feature” is. I often use words like “concept” or “style”, or establish metaphors to debugging programs or making fMRI scans of brains. Both metaphors help people outside of the subfield un... See more