Tatsunori Hashimoto
Assistant professor at Stanford CS; co-author of HELM, AlpacaEval, and many influential papers on LLM evaluation, alignment, and generalization.
- Role
- professor
- Currently at
- Stanford University
- twitter.com/tatsu_hashimoto
- GitHub
- github.com/tatsu-lab
- Scholar
- scholar.google.com/citations
- Papers
- 31
Cite
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Authored papers
31Towards Execution-Grounded Automated AI Research
arXiv 2026
s1: Simple Test-Time Scaling
preprint
OpenThoughts: Data Recipes for Reasoning Models
arXiv 2025
One-Minute Video Generation with Test-Time Training
CVPR 2025 1
End-to-End Test-Time Training for Long Context
arXiv 2025
The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas
arXiv 2025
Reasoning to Learn from Latent Thoughts
arXiv 2025
Auditing Prompt Caching in Language Model APIs
arXiv 2025
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users
arXiv 2025
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
COLM
A Survey on Data Selection for Language Models
arXiv 2024
Learning to (Learn at Test Time): RNNs with Expressive Hidden States
arXiv 2024
Synthetic continued pretraining
arXiv 2024
Benchmarking Distributional Alignment of Large Language Models
arXiv 2024
Observational Scaling Laws and the Predictability of Language Model Performance
arXiv 2024
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models
arXiv 2024
Improving Pretraining Data Using Perplexity Correlations
arXiv 2024
Linguistic Calibration of Long-Form Generations
arXiv 2024
Evaluating Self-Supervised Learning via Risk Decomposition
arXiv 2023
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
arXiv 2023
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
arXiv 2023
Learning to (Learn at Test Time)
arXiv 2023
Whose Opinions Do Language Models Reflect?
arXiv 2023
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
arXiv 2023
Robust Distortion-free Watermarks for Language Models
arXiv 2023
On the Learnability of Watermarks for Language Models
arXiv 2023
Out-of-Domain Robustness via Targeted Augmentations
arXiv 2023
Contrastive Decoding: Open-ended Text Generation as Optimization
arXiv 2022
Language modeling via stochastic processes
language-modeling-via-stochastic-processes
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
arXiv 2022
Large Language Models Can Be Strong Differentially Private Learners
large-language-models-can-be-strong
Tool contributions
1Affiliations
Frequent co-authors
10from 31 papers
Percy Liang
professor
Carlos Guestrin
Xiang Lisa Li
researcher
Karan Dalal
Sanmi Koyejo
professor
Xiaolong Wang
Yann Dubois
researcher
Yejin Choi
professor
Yu Sun
Chris J. Maddison