Tatsunori Hashimoto
Assistant Professor of Computer Science
Studies language model training and evaluation, including robustness and instruction-following models.
Tatsunori Hashimoto's lab at Stanford works on interpretability, evaluation & safety of LMs, large language models and trustworthy & safe ML. 24 papers since 2023, 20 at top venues, mostly ICLR and ICML. 9 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 83%
- PhD students
- 9
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 11 papers2024 · T1 11
- 13 papers2023 · T1 9
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, ACL, EACL
Research focuspaper titles that use each topic's words
- Large language models15 papers
- Interpretability, evaluation & safety of LMs1 paper
- Trustworthy & safe ML0 papers
- model training0 papers
- studies language0 papers
Paper list
From the lab homepage.
202411 papers
- ICLR (T1)Benchmarking and improving generator-validator consistency of language models (opens in a new tab)
- JMLR (T1)Foundation Models and Fair Use (opens in a new tab)
- ICLR (T1)Identifying the risks of lm agents with an lm-emulated sandbox (opens in a new tab)
- ICML (T1)Language Models with Conformal Factuality Guarantees (opens in a new tab)
- ICML (T1)Linguistic Calibration of Language Models (opens in a new tab)
- ICLR (T1)On the learnability of watermarks for language models (opens in a new tab)
- ICLR (T1)One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention (opens in a new tab)
- ICLR (T1)Proving test set contamination in black box language models (opens in a new tab)
- TMLR (T1)Robust Distortion-free Watermarks for Language Models (opens in a new tab)
- ICLR (T1)Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions (opens in a new tab)
- ICML (T1)Trustless Audits without Revealing Data or Models (opens in a new tab)
202313 papers
- NeurIPS (T1)AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback (opens in a new tab)
- Transactions of the Association of Computational LinguisticsBenchmarking Large Language Models for News Summarization (opens in a new tab)
- ICML (T1)Coder Reviewer Reranking for Code Generation (opens in a new tab)
- International Conference on Machine Learning (ICML , oral)"Data Feedback Loops: Model-driven Amplification of Dataset Biases (opens in a new tab)
- FAccTEasily accessible text-to-image generation amplifies demographic stereotypes at large scale (opens in a new tab)
- ICML (T1)Evaluating Self-Supervised Learning via Risk Decomposition (opens in a new tab)
- ICLR (T1)Is a caption worth a thousand images? a controlled study for representation learning (opens in a new tab)
- NeurIPS (T1)Likelihood-Based Diffusion Language Models (opens in a new tab)
- EMNLP (T1)Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models (opens in a new tab)
- ICML (T1)Out-of-Domain Robustness via Targeted Augmentations (opens in a new tab)
- ACL (T1)Tracing and Removing Data Errors in Natural Language Generation Datasets (opens in a new tab)
- EACLWhen Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to StanfordFall 2027
Stanford Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- See program page
Confirmed on the program page.
Similar labsnearest by research summary
- CHETH Zurich
Ryan Cotterell
- Multilingual, translation & linguistics
- USUCSD
Jingbo Shang
- Knowledge, QA & retrieval
- CNTsinghua
Tianxing He
- Large language models
- CNPeking
Xu Sun
- Dialogue, generation & summarization
- USUMD
Sarah Wiegreffe
- Large language models
- USUCSD
Prithviraj Ammanabrolu
- Reasoning & agents
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
Stanfordcloses