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Luke Zettlemoyer

Identifiers

  • name variant Luke Zettlemoyer 0.60 · backfill

Papers (73)

  1. M*: A Modular, Extensible, Serving System for Multimodal Models cs.LG · 2026 · author #8
  2. Scaling Participation in Modular AI Systems cs.AI · 2026 · author #4
  3. JobBench: Aligning Agent Work With Human Will cs.AI · 2026 · author #19
  4. Slicing and Dicing: Configuring Optimal Mixtures of Experts cs.LG · 2026 · author #4
  5. Fast Byte Latent Transformer cs.CL · 2026 · author #5
  6. Compute Optimal Tokenization cs.CL · 2026 · author #9
  7. Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation cs.CV · 2026 · author #14
  8. Micro Language Models Enable Instant Responses cs.CL · 2026 · author #5
  9. Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons cs.RO · 2026 · author #9
  10. Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model cs.CL · 2026 · author #5
  11. MoCo: A One-Stop Shop for Model Collaboration Research cs.CL · 2026 · author #18
  12. Olmo 3 cs.CL · 2025 · author #63
  13. DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research cs.CL · 2025 · author #18
  14. Reconstruction Alignment Improves Unified Multimodal Models cs.CV · 2025 · author #3
  15. Spurious Rewards: Rethinking Training Signals in RLVR cs.AI · 2025 · author #14
  16. DreamGen: Unlocking Generalization in Robot Learning through Video World Models cs.RO · 2025 · author #23
  17. s1: Simple test-time scaling cs.CL · 2025 · author #7
  18. 2 OLMo 2 Furious cs.CL · 2024 · author #40
  19. Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models cs.CL · 2024 · author #10
  20. Latent Action Pretraining from Videos cs.RO · 2024 · author #14
  21. Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model cs.AI · 2024 · author #9
  22. DataComp-LM: In search of the next generation of training sets for language models cs.LG · 2024 · author #44
  23. OLMo: Accelerating the Science of Language Models cs.CL · 2024 · author #38
  24. Detecting Pretraining Data from Large Language Models cs.CL · 2023 · author #8
  25. Demystifying CLIP Data cs.CV · 2023 · author #9
  26. QLoRA: Efficient Finetuning of Quantized LLMs cs.LG · 2023 · author #4
  27. LIMA: Less Is More for Alignment cs.CL · 2023 · author #14
  28. ART: Automatic multi-step reasoning and tool-use for large language models cs.CL · 2023 · author #5
  29. Toolformer: Language Models Can Teach Themselves to Use Tools cs.CL · 2023 · author #6
  30. REPLUG: Retrieval-Augmented Black-Box Language Models cs.CL · 2023 · author #7
  31. OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization cs.CL · 2022 · author #17
  32. LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cs.LG · 2022 · author #4
  33. OPT: Open Pre-trained Transformer Language Models cs.CL · 2022 · author #19
  34. InCoder: A Generative Model for Code Infilling and Synthesis cs.SE · 2022 · author #9
  35. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? cs.CL · 2022 · author #7
  36. Unsupervised Cross-lingual Representation Learning at Scale cs.CL · 2019 · author #9
  37. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension cs.CL · 2019 · author #8
  38. RoBERTa: A Robustly Optimized BERT Pretraining Approach cs.CL · 2019 · author #9
  39. Multi-hop Reading Comprehension through Question Decomposition and Rescoring cs.CL · 2019 · author #3
  40. Compositional Questions Do Not Necessitate Multi-hop Reasoning cs.CL · 2019 · author #6
  41. Better Character Language Modeling Through Morphology cs.CL · 2019 · author #2
  42. Evaluating Gender Bias in Machine Translation cs.CL · 2019 · author #3
  43. Cloze-driven Pretraining of Self-attention Networks cs.CL · 2019 · author #4
  44. Improving Semantic Parsing for Task Oriented Dialog cs.CL · 2019 · author #7
  45. The Referential Reader: A Recurrent Entity Network for Anaphora Resolution cs.CL · 2019 · author #2
  46. pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference cs.CL · 2018 · author #5
  47. Syntactic Scaffolds for Semantic Structures cs.CL · 2018 · author #4
  48. Neural Metaphor Detection in Context cs.CL · 2018 · author #4
  49. Mapping Language to Code in Programmatic Context cs.CL · 2018 · author #4
  50. Dissecting Contextual Word Embeddings: Architecture and Representation cs.CL · 2018 · author #3
  51. QuAC : Question Answering in Context cs.CL · 2018 · author #8
  52. Ultra-Fine Entity Typing cs.CL · 2018 · author #4
  53. Large-Scale QA-SRL Parsing cs.CL · 2018 · author #4
  54. Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling cs.CL · 2018 · author #4
  55. Deep RNNs Encode Soft Hierarchical Syntax cs.CL · 2018 · author #3
  56. Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum cs.CL · 2018 · author #4
  57. SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach cs.CL · 2018 · author #2
  58. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks cs.CL · 2018 · author #4
  59. Higher-order Coreference Resolution with Coarse-to-fine Inference cs.CL · 2018 · author #3
  60. AllenNLP: A Deep Semantic Natural Language Processing Platform cs.CL · 2018 · author #9
  61. NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System cs.CL · 2018 · author #3
  62. Deep contextualized word representations cs.CL · 2018 · author #7
  63. Crowdsourcing Question-Answer Meaning Representations cs.CL · 2017 · author #5
  64. End-to-end Neural Coreference Resolution cs.CL · 2017 · author #4
  65. Zero-Shot Relation Extraction via Reading Comprehension cs.CL · 2017 · author #4
  66. Recurrent Additive Networks cs.CL · 2017 · author #3
  67. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension cs.CL · 2017 · author #4
  68. Learning a Neural Semantic Parser from User Feedback cs.CL · 2017 · author #5
  69. Neural AMR: Sequence-to-Sequence Models for Parsing and Generation cs.CL · 2017 · author #5
  70. Commonly Uncommon: Semantic Sparsity in Situation Recognition cs.CV · 2016 · author #3
  71. A Theme-Rewriting Approach for Generating Algebra Word Problems cs.CL · 2016 · author #3
  72. Global Neural CCG Parsing with Optimality Guarantees cs.CL · 2016 · author #3
  73. Extreme Extraction: Only One Hour per Relation cs.CL · 2015 · author #2

Mentions

  • 2509.07295 #3 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2606.12688 #8 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2606.07812 #4 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 1506.06418 #2 · backfill · confidence 0.70 Luke Zettlemoyer
  • 2605.01188 #9 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2605.26329 #19 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2602.07120 #5 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2604.24763 #14 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2511.19399 #18 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2411.04996 #10 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2406.11794 #44 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2310.16789 #8 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2301.12652 #7 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2305.11206 #14 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2212.12017 #17 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2402.00838 #38 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2303.09014 #5 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 1911.02116 #9 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2506.10947 #14 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2309.16671 #9 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2204.05999 #9 · arxiv_oai · confidence 0.70 Luke Zettlemoyer
  • 2505.12705 #23 · arxiv_oai · confidence 0.70 Luke Zettlemoyer

Frequent Coauthors