Luke Zettlemoyer
Professor
Researches large language models, semantic parsing and multimodal pretraining.
Luke Zettlemoyer's lab at UW works on large language models, multilingual, translation & linguistics and vision-language & multimodal. 87 papers since 2023, 72 at top venues, mostly ICLR and NeurIPS. 34 alumni. Applications close .
At a glance
- papers, 2023–2026
- 23/ year
- at T1 venues
- 83%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 34
Papers by yearfrom DBLP, 2023–2026
- 5 papers2026 · T1 5 · collecting
- 22 papers2025 · T1 19
- 24 papers2024 · T1 22
- 36 papers2023 · T1 26
T1 venuesOtherVenues: ICLR, NeurIPS, ACL, EMNLP, Findings of EMNLP
Research focuspaper titles that use each topic's words
- Large language models45 papers
- Vision-language & multimodal39 papers
- Multilingual, translation & linguistics6 papers
- pretraining5 papers
- researches large0 papers
Paper list
From DBLP.
20265 papers
- ICLR (T1)High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning (opens in a new tab)
- ICLR (T1)Latent Speech-Text Transformer (opens in a new tab)
- CVPR (T1)Multimodal RewardBench 2: Evaluating Omni Reward Models for Interleaved Text and Image (opens in a new tab)
- ICLR (T1)Reconstruction Alignment Improves Unified Multimodal Models (opens in a new tab)
- CVPR (T1)TV2TV: A Unified Framework for Interleaved Language and Video Generation (opens in a new tab)
202522 papers
- ICLR (T1)(Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning (opens in a new tab)
- ACL (T1)Byte Latent Transformer: Patches Scale Better Than Tokens (opens in a new tab)
- NeurIPS (T1)CAT: Content-Adaptive Image Tokenization (opens in a new tab)
- NAACL (T1)Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models (opens in a new tab)
- ICLR (T1)Fantastic Copyrighted Beasts and How (Not) to Generate Them (opens in a new tab)
- NeurIPS (T1)FlexOLMo: Open Language Models for Flexible Data Use (opens in a new tab)
- ICLR (T1)Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass (opens in a new tab)
- NeurIPS (T1)Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems (opens in a new tab)
- ACL (T1)Improving Factuality with Explicit Working Memory (opens in a new tab)
- ICLR (T1)Latent Action Pretraining from Videos (opens in a new tab)
- NeurIPS (T1)LMFusion: Adapting Pretrained Language Models for Multimodal Generation (opens in a new tab)
- ICML (T1)Memory Layers at Scale (opens in a new tab)
202424 papers
- EMNLP (T1)Altogether: Image Captioning via Re-aligning Alt-text (opens in a new tab)
- Findings of EMNLPBetter Alignment with Instruction Back-and-Forth Translation (opens in a new tab)
- EMNLP (T1)Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models (opens in a new tab)
- EMNLP (T1)CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation (opens in a new tab)
- NeurIPS (T1)DataComp-LM: In search of the next generation of training sets for language models (opens in a new tab)
- ICLR (T1)Demystifying CLIP Data (opens in a new tab)
- ICLR (T1)Detecting Pretraining Data from Large Language Models (opens in a new tab)
- ACL (T1)Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (opens in a new tab)
- NeurIPS (T1)Evaluating Copyright Takedown Methods for Language Models (opens in a new tab)
- ICLR (T1)In-Context Pretraining: Language Modeling Beyond Document Boundaries (opens in a new tab)
- NeurIPS (T1)Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length (opens in a new tab)
- CVPR (T1)MoDE: CLIP Data Experts via Clustering (opens in a new tab)
202336 papers
- ICLR (T1)AGRO: Adversarial discovery of error-prone Groups for Robust Optimization (opens in a new tab)
- ICLR (T1)Binding Language Models in Symbolic Languages (opens in a new tab)
- ICCV (T1)CiT: Curation in Training for Effective Vision-Language Data (opens in a new tab)
- ACL (T1)Contrastive Decoding: Open-ended Text Generation as Optimization (opens in a new tab)
- ACL (T1)CREPE: Open-Domain Question Answering with False Presuppositions (opens in a new tab)
- Findings of EMNLPDemystifying Prompts in Language Models via Perplexity Estimation (opens in a new tab)
- ICML (T1)DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation (opens in a new tab)
- EMNLP (T1)FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (opens in a new tab)
- Findings of EMNLPGetting MoRE out of Mixture of Language Model Reasoning Experts (opens in a new tab)
- Findings of ACLIn-context Examples Selection for Machine Translation (opens in a new tab)
- ICLR (T1)InCoder: A Generative Model for Code Infilling and Synthesis (opens in a new tab)
- IEEE ACM Trans. Audio Speech Lang. Process.LegoNN: Building Modular Encoder-Decoder Models (opens in a new tab)
Where alumni went34 listed
Apply to UWFall 2027
UW Allen School Computer Science PhD
- Round
- Single round
- Opens
- GRE
- not required
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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