Percy Liang
In plain wordsAn American computer scientist at Stanford whose center coined the term 'foundation model' and builds tools for evaluating and documenting large AI models.
American computer scientist at Stanford University and director of its Center for Research on Foundation Models (CRFM), founded in 2021. After a PhD at the University of California, Berkeley, on learning semantic parsers, he became known for work on natural language understandingnatural language understanding, n.: The part of language AI concerned with working out what a piece of text means — who did what to whom, what the writer wants and what follows from it — rather than with producing text. and reliable machine learning, including the SQuADSQuAD, prop. n.: A Stanford collection of more than 100,000 questions about Wikipedia articles, each solved by highlighting the words in the article that give the answer; it drove rapid progress in machine reading from 2016. reading-comprehension benchmark (2016, with Pranav Rajpurkar and others) and the use of influence functionsinfluence function, n.: A mathematical shortcut for estimating how much a single training example shaped a model's behavior, for instance how a prediction would change if that example had been left out, without retraining the model. to explain neural-network predictions (2017, with Pang Wei Koh). With Rishi Bommasani and more than 100 co-authors he named and analyzed foundation modelsfoundation model, n.: A very large AI model, built once at great cost from vast amounts of text, images or code gathered mostly from the internet, that many products then reuse as their starting point, from chatbots to coding tools. (2021), and CRFM went on to build the HELMHELM, prop. n.: A large, open testing framework from Stanford that grades many language models on many kinds of tasks and qualities — not only accuracy but also fairness, toxicity and efficiency. evaluation framework (2022), the low-cost instruction-following model Alpaca (2023) and the Foundation Model Transparency Index (2023). He co-founded the AI cloud company Together AI and advocates openness and transparency in model development.
Related
- foundation modelfoundation model, n.: A very large AI model, built once at great cost from vast amounts of text, images or code gathered mostly from the internet, that many products then reuse as their starting point, from chatbots to coding tools.
- HELMHELM, prop. n.: A large, open testing framework from Stanford that grades many language models on many kinds of tasks and qualities — not only accuracy but also fairness, toxicity and efficiency.
- SQuADSQuAD, prop. n.: A Stanford collection of more than 100,000 questions about Wikipedia articles, each solved by highlighting the words in the article that give the answer; it drove rapid progress in machine reading from 2016.
- influence functioninfluence function, n.: A mathematical shortcut for estimating how much a single training example shaped a model's behavior, for instance how a prediction would change if that example had been left out, without retraining the model.
- benchmarkbenchmark, n.: A fixed, shared test — a set of questions or tasks with an agreed way of scoring — used to compare AI systems with one another and to track progress over time.
- open-weight modelopen-weight model, n.: An AI model whose trained internal numbers are published for anyone to download and run on their own computers, even if the data and code used to make it stay private.
Sources
- Bommasani, R. et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv preprint.
- Liang, P. et al. (2023). Holistic Evaluation of Language Models. Transactions on Machine Learning Research.
Mentioned in
Michael I. JordanMichael I. Jordan, prop. n.: An American computer scientist and statistician who did much to bring probability and statistics into machine learning, and who trained many of the field's leaders.