--- title: "Luke Zettlemoyer — papers, page 4 · University of Washington" description: "Luke Zettlemoyer, University of Washington: 87 papers on Elamur Research (2008–2025), h-index 91 — affiliations, topics, co-authors and…" canonical: "https://research.elamur.ai/free/en/authors/luke-zettlemoyer/page/4/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/luke-zettlemoyer/page/4/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) / page 4 # Luke Zettlemoyer · page 4 87 papers in our library · 2008–2025 · page 4 of 4 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/page/3/) [1](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) [2](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/page/2/) [3](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/page/3/) **4** 76. **[Questions Are All You Need to Train a Dense Passage Retriever](https://md.research.elamur.ai/free/en/papers/sachan-2023-questions-are-all-you-need/)** *(2023 · 65 citations)* Dense passage retrievers for open-domain QA usually depend on large labeled sets of question–passage pairs, plus extra machinery such as hard-negative mining and cleaning of positive examples. 77. **[Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens](https://md.research.elamur.ai/free/en/papers/liu-2024-infini-gram-scaling-unbounded-n/)** *(2024 · 62 citations)* Classical n-gram language models are usually limited to small n (often 5) and modest data, because count tables grow explosively with n. 78. **[In-context Pretraining: Language Modeling Beyond Document Boundaries](https://md.research.elamur.ai/free/en/papers/shi-2023-context-pretraining-language-modeling-document/)** *(2023 · 61 citations)* Standard LM pretraining packs randomly chosen documents into each context window. Earlier documents therefore tell the model nothing about later ones, so the model gets no practice using information across document boundaries. The authors (Meta AI, University of Washington, AI2) propose In-Context Pretraining. It… 79. **[Shepherd: A Critic for Language Model Generation](https://md.research.elamur.ai/free/en/papers/wang-2023-shepherd-critic-language-model-generation/)** *(2023 · 60 citations)* Shepherd tackles the problem of automatically critiquing text produced by large language models, which still often generate false or incoherent answers. 80. **[Reliable, Adaptable, and Attributable Language Models with Retrieval](https://md.research.elamur.ai/free/en/papers/asai-2024-reliable-adaptable-attributable-language-models/)** *(2024 · 45 citations)* This position paper by Asai et al. contends that retrieval-augmented LMs should succeed purely parametric LMs as the main design for future language models. It contains no new experiments, datasets, or models. Instead, it surveys prior work and lists five weaknesses of parametric models: hallucinated facts, outputs… 81. **[Scaling Laws for Generative Mixed-Modal Language Models](https://md.research.elamur.ai/free/en/papers/aghajanyan-2023-scaling-laws-generative-mixed-modal/)** *(2023 · 45 citations)* Single generative models can treat images, speech, code and text as token streams. It is unclear, however, whether added scale overcomes the interference that arises when several modalities share one network. The authors ran more than 250 training experiments covering seven modalities: Text, Image, Image-Text… 82. **[Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models](https://md.research.elamur.ai/free/en/papers/blevins-2024-breaking-curse-multilinguality-cross-lingual/)** *(2024 · 42 citations)* Multilingual language models fit many languages into one fixed set of parameters, so each language usually ends up weaker than it would be in a dedicated monolingual model. 83. **[QANom: Question-Answer driven SRL for Nominalizations](https://md.research.elamur.ai/free/en/papers/klein-2020-qanom-question-answer-driven-srl/)** *(2020 · 41 citations)* QANom extends QA-SRL from verbs to nouns derived from verbs: a noun like proof or increase is questioned with its verb's question templates («What has someone proved?», «Why did something increase?») and answered with spans of the sentence. 84. **[Active Learning for Coreference Resolution using Discrete Annotation](https://md.research.elamur.ai/free/en/papers/li-2020-active-learning-coreference-resolution-discrete/)** *(2020 · 36 citations)* Labeling coreference exhaustively is costly, because annotators must follow entity chains across long texts. 85. **[The Referential Reader: A Recurrent Entity Network for Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/liu-2019-referential-reader-recurrent-entity-network/)** *(2019 · 31 citations)* Most coreference systems use the mention-pair approach, which scores every pair of spans. This costs a lot of computation and annotated data, and it does not match how people read, which is roughly word by word. The authors propose the Referential Reader, which processes text strictly left to right and keeps a small… 86. **[MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling](https://md.research.elamur.ai/free/en/papers/limisiewicz-2024-myte-morphology-driven-byte-encoding/)** *(2024 · 30 citations)* Byte-level multilingual language models avoid the coverage gaps of subword vocabularies, but they inherit a bias built into UTF-8. 87. **[Comparing Hallucination Detection Metrics for Multilingual Generation](https://md.research.elamur.ai/free/en/papers/kang-2024-comparing-hallucination-detection-metrics-multilingual/)** *(2024 · 18 citations)* Methods for spotting factual hallucinations in LLM output have mostly been tested on English, so it is unclear how well they work in other languages. [← prev](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/page/3/) [1](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) [2](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/page/2/) [3](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/page/3/) **4**