Percy Liang
Thomas M. Siebel Professor of Machine Learning and Senior Fellow at the Stanford Institute for Human-Centered AI
Studies foundation models and their holistic evaluation, including language model transparency and agents.
Percy Liang's lab at Stanford works on large language models, reasoning & agents and generative & foundation models. 88 papers since 2023, 68 at top venues, mostly ICLR and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 23/ year
- at T1 venues
- 77%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 12 papers2026 · T1 8 · collecting
- 23 papers2025 · T1 20
- 23 papers2024 · T1 15
- 30 papers2023 · T1 25
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, ACL, AAAI/ACM AIES
Research focuspaper titles that use each topic's words
- Large language models43 papers
- Generative & foundation models37 papers
- language model7 papers
- Reasoning & agents6 papers
- transparency4 papers
Paper list
From DBLP.
202612 papers
- FAccTAlgorithmic Monocultures in Hiring (opens in a new tab)
- Findings of EACLBeyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pre-training (opens in a new tab)
- ICLR (T1)Comparing AI Agents to Cybersecurity Professionals in Real-World Penetration Testing (opens in a new tab)
- ICLR (T1)Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning (opens in a new tab)
- ICLR (T1)Fantastic Pretraining Optimizers and Where to Find Them (opens in a new tab)
- ICLR (T1)Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation (opens in a new tab)
- ICLR (T1)MLE-Smith: Scaling MLE Tasks with Automated Multi-agent Pipeline (opens in a new tab)
- ICLR (T1)Pre-training under infinite compute (opens in a new tab)
- ICLR (T1)Reinforcement Learning for Machine Learning Engineering Agents (opens in a new tab)
- Trans. Mach. Learn. Res.SpecEval: Evaluating Model Adherence to Behavior Specifications (opens in a new tab)
- Trans. Mach. Learn. Res.The 2025 Foundation Model Transparency Index (opens in a new tab)
- ICLR (T1)WorldGym: World Model as An Environment for Policy Evaluation (opens in a new tab)
202523 papers
- ICLR (T1)AIR-BENCH 2024: A Safety Benchmark based on Regulation and Policies Specified Risk Categories (opens in a new tab)
- ICML (T1)Auditing Prompt Caching in Language Model APIs (opens in a new tab)
- NeurIPS (T1)Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives (opens in a new tab)
- ICLR (T1)AutoBencher: Towards Declarative Benchmark Construction (opens in a new tab)
- ICLR (T1)BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments (opens in a new tab)
- NeurIPS (T1)Blackbox Model Provenance via Palimpsestic Membership Inference (opens in a new tab)
- NeurIPS (T1)BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems (opens in a new tab)
- ICML (T1)Eliciting Language Model Behaviors with Investigator Agents (opens in a new tab)
- NeurIPS (T1)Establishing Best Practices in Building Rigorous Agentic Benchmarks (opens in a new tab)
- ICML (T1)Independence Tests for Language Models (opens in a new tab)
- ICML (T1)Language Models May Verbatim Complete Text They Were Not Explicitly Trained On (opens in a new tab)
- Findings of NAACLLawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain (opens in a new tab)
202423 papers
- Trans. Mach. Learn. Res.Anticipatory Music Transformer (opens in a new tab)
- ICLR (T1)Benchmarking and Improving Generator-Validator Consistency of Language Models (opens in a new tab)
- Trans. Assoc. Comput. LinguisticsBenchmarking Large Language Models for News Summarization (opens in a new tab)
- AAAI/ACM AIESEcosystem Graphs: Documenting the Foundation Model Supply Chain (opens in a new tab)
- NeurIPS (T1)Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making (opens in a new tab)
- AAAI/ACM AIESFoundation Model Transparency Reports (opens in a new tab)
- NeurIPS (T1)Image2Struct: Benchmarking Structure Extraction for Vision-Language Models (opens in a new tab)
- ICLR (T1)Large Language Models as Analogical Reasoners (opens in a new tab)
- Trans. Assoc. Comput. LinguisticsLost in the Middle: How Language Models Use Long Contexts (opens in a new tab)
- AAAI (T1)MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records (opens in a new tab)
- ICML (T1)MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation (opens in a new tab)
- ICLR (T1)On the Learnability of Watermarks for Language Models (opens in a new tab)
202330 papers
- NeurIPS (T1)AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback (opens in a new tab)
- ACL (T1)Are Sample-Efficient NLP Models More Robust? (opens in a new tab)
- ACL (T1)Backpack Language Models (opens in a new tab)
- Findings of ACLBeyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models (opens in a new tab)
- Trans. Mach. Learn. Res.Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (opens in a new tab)
- NeurIPS (T1)Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models (opens in a new tab)
- ICML (T1)CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks (opens in a new tab)
- ACL (T1)Contrastive Decoding: Open-ended Text Generation as Optimization (opens in a new tab)
- NeurIPS (T1)Data Selection for Language Models via Importance Resampling (opens in a new tab)
- ACL (T1)Do Question Answering Modeling Improvements Hold Across Benchmarks? (opens in a new tab)
- NeurIPS (T1)DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining (opens in a new tab)
- NeurIPS (T1)Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to StanfordFall 2027
Stanford Computer Science PhD
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- Opens
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Confirmed on the program page.
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Data
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- The alumni list could not be found on the lab site.
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