Ian Tenney
I am a Staff Research Scientist on the People + AI Research (PAIR) team at Google DeepMind. My group focuses on interpretability for large langauge models (LLMs), including visualization tools, attribution methods, and intrinsic analysis (a.k.a. BERTology) of model representations. Through these, we aim to answer questions like:
- Why did a model make this particular prediction?
- What kind of knowledge is stored in the parameters, and how is it represented and reasoned about?
- How can we build our own mental models of how - and when - AI works?
Among other things, I am a co-creator of the Learning Interpretability Tool (LIT) and author of BERT Rediscovers the Classical NLP Pipeline.
Previously, I’ve taught an NLP course at UC Berkeley School of Information. In a past life I was a physicist, studying ultrafast molecular and optical physics in the lab of Philip H. Bucksbaum at Stanford / SLAC.
When I’m not behind a computer I enjoy hiking and photography; you can find some of it here.
Contact: "if" + lastname + "@gmail.com"
(or @google.com
)
news
May 16, 2024 | We’ve open-sourced LLM Comparator, a visualization tool to help LLM developers make sense of side-by-side evaluations. Learn more in our blog post and at goo.gle/llm-comparator, or jump in and try the in-browser demo. |
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Apr 15, 2024 | New preprint! Interactive Prompt Debugging with Sequence Salience goes into more detail on the prompt debugging tool we previously released for Gemma. Sequence Salience now works for Mistral and Llama 2, and features a more in-depth tutorial at goo.gle/sequence-salience. |
Feb 21, 2024 | LIT v1.1 featured in The Keyword as the debugging tool for the new Gemma family of open models from Google. As part of the Responsible Generative AI Toolkit, use the new sequence salience feature to debug complex LLM prompts, such as few-shot, chain-of-thought, or constitutions. Try it in Colab here: Using LIT to Analyze Gemma Models in Keras |
projects
selected publications
- LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language ModelsIEEE Transactions on Visualization and Computer Graphics, 2024
- The MultiBERTs: BERT Reproductions for Robustness AnalysisICLR (spotlight), 2022
- The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP ModelsIn Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020