---
title: "Who cites Clara Meister"
description: "350 papers of the Elamur Research library cite the works of Clara Meister, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/clara-meister/cited-by/"
---
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# Who cites Clara Meister

433 citations of their works from 350 papers of our library (of the 140,884 whose bibliographies we read) · 29 with a page here, the most cited first · [← Clara Meister](https://md.research.elamur.ai/free/en/authors/clara-meister/)

1. [**A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,221 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
2. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
3. [**OPT: Open Pre-trained Transformer Language Models**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,709 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
4. [**Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads**Tianle Cai, Yuhong Li, Zhengyang Geng, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-medusa-simple-llm-inference-acceleration/)
5. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
6. [**Towards Faithful Model Explanation in NLP: A Survey**Qing Lyu, Marianna Apidianaki, Chris Callison-Burch*2024 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/)
7. [**Testing the Predictions of Surprisal Theory in 11 Languages**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/)
8. [**Locally Typical Sampling**Clara Meister, Tiago Pimentel, Gian Wiher, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/)
9. [**Self-Evaluation Guided Beam Search for Reasoning**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/)
10. [**Quality-Aware Decoding for Neural Machine Translation**Patrick Fernandes, António Farinhas, Ricardo Rei, et al.*2022 · 143 citations*](https://md.research.elamur.ai/free/en/papers/fernandes-2022-quality-aware-decoding-neural-machine/)
11. [**NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics**Ximing Lu, Sean Welleck, Peter West, et al.*2022 · 136 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/)
12. [**LENS: A Learnable Evaluation Metric for Text Simplification**Mounica Maddela, Yao Dou, David Heineman, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/maddela-2023-lens-learnable-evaluation-metric-text/)
13. [**On the Proper Treatment of Tokenization in Psycholinguistics**Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2024-proper-treatment-tokenization-psycholinguistics/)
14. [**The Impact of Token Granularity on the Predictive Power of Language Model Surprisal**Byung-Doh Oh, William Schuler*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/oh-2025-impact-token-granularity-predictive-power/)
15. [**Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models**Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/padovani-2025-child-directed-language-does-not/)
16. [**ALADAN at IWSLT24 Low-resource Arabic Dialectal Speech Translation Task**Waad Ben Kheder, Josef Jon, André Beyer, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/kheder-2024-aladan-iwslt24-low-resource-arabic/)
17. [**An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal**Ryo Yoshida, Shinnosuke Isono, Taiga Someya, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yoshida-2026-existence-proof-neural-language-models/)
18. [**Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal**Sathvik Nair, Byung-Doh Oh*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nair-2026-clozing-gap-exploring-why-language/)
19. [**Probing for Reading Times**Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tsipidi-2026-probing-reading-times/)
20. [**Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets**Omar Momen, Emilie Sitter, Berenike Herrmann, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/momen-2026-surprisal-metaphor-novelty-judgments-moderate/)
21. [**Aggregated Transformer Attention Measures Predict Reading Times Beyond Surprisal**Lukas Mielczarek, Laura Kallmeyer*2026*](https://md.research.elamur.ai/free/en/papers/mielczarek-2026-aggregated-transformer-attention-measures-predict/)
22. [**NegNLI-BR: A Brazilian Portuguese Benchmark for Negation in Natural Language Inference**Matheus Westhelle, Viviane Moreira*2026*](https://md.research.elamur.ai/free/en/papers/westhelle-2026-negnli-br-brazilian-portuguese-benchmark/)
23. [**Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic Cues**Yu Wang, Olcay Türk, Angela Grimminger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-predicting-states-understanding-explanatory-interactions/)
24. [**Reading Time in the Wild: An Assessment of Readability Predictors Based on Naturally-Observed Reading Times**Sijbren van Vaals, Rik van Noord, Malvina Nissim*2026*](https://md.research.elamur.ai/free/en/papers/vaals-2026-reading-time-wild-assessment-readability/)
25. [**Semantic Information: A Difference That Makes a Difference**J. Nathanael Philipp, Max Kölbl, Michael Richter*2026*](https://md.research.elamur.ai/free/en/papers/philipp-2026-semantic-information-difference-that-makes/)
26. [**The Frequency Confound in Language-Model Surprisal and Metaphor Novelty**Omar Momen, Sina Zarrieß*2026*](https://md.research.elamur.ai/free/en/papers/momen-2026-frequency-confound-language-model-surprisal/)
27. [**Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data**Wajdi Zaghouani*2026*](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-cognitive-alignment-large-language-models/)
28. [**Word predictability estimates from language models are not robust to tokenizer vocabulary**Kien Nguyen, Suhas Arehalli*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-word-predictability-estimates-language-models/)
29. [**Assessing the Agreement Competence of Large Language Models**Alba Táboas García, Leo Wanner*2025*](https://md.research.elamur.ai/free/en/papers/garcia-2025-assessing-agreement-competence-large-language/)

321 more citing papers of our library have no page here yet.

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