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Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models

Sang T. Truong, Duc Q. Nguyen, Toan Nguyen, Dong D. Le, Nhi N. Truong, Tho T. Quan, Sanmi Koyejo

Fine-tuning LLaMa-2, Gemma, Mixtral for Vietnamese and evaluation Vietnamese large language models across 10 tasks with 31 distinct metrics

Stanford AI Lab Papers and Talks at ICLR 2024

Compiled by James Burgess

All the great work from the Stanford AI Lab accepted at ICLR 2024, all in one place.

Stanford AI Lab Graduates 2024

Compiled by Judy Hanwen Shen & John Hewitt

A list of our excellent students graduating roughly in summer 2024.

Clover: Closed-Loop Verifiable Code Generation

Chuyue Sun, Ying Sheng

Clover paradigm checks consistencies among code, docstrings and annotations and enforces correctness in AI-generated code.

Stanford AI Lab Papers and Talks at NeurIPS 2023

Compiled by Ruhana Azam

All the great work from the Stanford AI Lab accepted at NeurIPS, all in one place.

Codebook Features: Sparse and Discrete Interpretability for Neural Networks

Alex Tamkin and Mohammad Taufeeque

A new interpretability method for training neural networks with sparse, discrete, and controllable hidden states.

Stanford AI Lab Papers and Talks at ICML 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at ICML 2023, all in one place.

Stanford AI Lab Papers and Talks at RSS 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at RSS 2023, all in one place.

Stanford AI Lab Papers and Talks at ACL 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at ACL 2023, all in one place.

Self-Improving Robots: Embracing Autonomy in Robot Learning

Archit Sharma

Learning methods for robotics have a data problem. Autonomous robotic systems can deliver orders of magnitude more real-world embodied data than we have.