---
title: "Who cites Kilian Q. Weinberger"
description: "4,122 papers of the Elamur Research library cite the works of Kilian Q."
canonical: "https://research.elamur.ai/free/en/authors/kilian-q-weinberger/cited-by/"
---
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# Who cites Kilian Q. Weinberger

4,126 citations of their works from 4,122 papers of our library (of the 140,884 whose bibliographies we read) · 284 with a page here, the most cited first · page 1 of 3 · 1–100 · [← Kilian Q. Weinberger](https://md.research.elamur.ai/free/en/authors/kilian-q-weinberger/)

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1. [**Swin Transformer: Hierarchical Vision Transformer using Shifted Windows**Ze Liu, Yutong Lin, Yue Cao, et al.*2021 · 33,826 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-swin-transformer-hierarchical-vision-transformer/)
2. [**Learning Transferable Architectures for Scalable Image Recognition**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,518 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/)
3. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
4. [**EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks**Mingxing Tan, Quoc V. Le*2019 · 5,308 citations*](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/)
5. [**Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks**Nils Reimers, Iryna Gurevych*2019 · 4,838 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/)
6. [**Neural Architecture Search with Reinforcement Learning**Barret Zoph, Quoc V. Le*2016 · 4,331 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
7. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
8. [**Robust Speech Recognition via Large-Scale Weak Supervision**Alec Radford, Jong Wook Kim, Tao Xu, et al.*2022 · 2,497 citations*](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/)
9. [**Making Pre-trained Language Models Better Few-shot Learners**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,952 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/)
10. [**COMET: A Neural Framework for MT Evaluation**Ricardo Rei, Craig Stewart, Ana C Farinha, et al.*2020 · 1,784 citations*](https://md.research.elamur.ai/free/en/papers/rei-2020-comet-neural-framework-mt-evaluation/)
11. [**CLIPScore: A Reference-free Evaluation Metric for Image Captioning**Jack Hessel, Ari Holtzman, Maxwell Forbes, et al.*2021 · 1,498 citations*](https://md.research.elamur.ai/free/en/papers/hessel-2021-clipscore-reference-free-evaluation-metric/)
12. [**DINOv2: Learning Robust Visual Features without Supervision**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,425 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
13. [**wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations**Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, et al.*2020 · 1,355 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2020-wav2vec-2-0-framework-self/)
14. [**Adversarial Learning for Neural Dialogue Generation**Jiwei Li, Will Monroe, Tianlin Shi, et al.*2017 · 1,044 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/)
15. [**GPT Understands, Too**Xiao Liu, Yanan Zheng, Zhengxiao Du, et al.*2021 · 1,039 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-gpt-understands-too/)
16. [**FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation**Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/)
17. [**Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 962 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/)
18. [**ImageBind: One Embedding Space To Bind Them All**Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, et al.*2023 · 943 citations*](https://md.research.elamur.ai/free/en/papers/girdhar-2023-imagebind-one-embedding-space-bind/)
19. [**Patient Knowledge Distillation for BERT Model Compression**Siqi Sun, Yu Cheng, Zhe Gan, et al.*2019 · 939 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-patient-knowledge-distillation-bert-model/)
20. [**A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity**Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.*2023 · 866 citations*](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
21. [**Segment Anything**Alexander Kirillov, Eric Mintun, Nikhila Ravi, et al.*2023 · 865 citations*](https://md.research.elamur.ai/free/en/papers/kirillov-2023-segment-anything/)
22. [**TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP**John X. Morris, Eli Lifland, Jin Yong Yoo, et al.*2020 · 857 citations*](https://md.research.elamur.ai/free/en/papers/morris-2020-textattack-framework-adversarial-attacks-data/)
23. [**MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance**Wei Zhao, Maxime Peyrard, Fei Liu, et al.*2019 · 822 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-moverscore-text-generation-evaluating-contextualized/)
24. [**BARTScore: Evaluating Generated Text as Text Generation**Weizhe Yuan, Graham Neubig, Pengfei Liu*2021 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
25. [**XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale**Arun Babu, Changhan Wang, Andros Tjandra, et al.*2021 · 799 citations*](https://md.research.elamur.ai/free/en/papers/babu-2021-xls-r-self-supervised-cross/)
26. [**Can Large Language Models Transform Computational Social Science?**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 776 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)
27. [**HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models**Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, et al.*2023 · 646 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/)
28. [**ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge**Yunxiang Li, Zihan Li, Kai Zhang, et al.*2023 · 640 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-chatdoctor-medical-chat-model-fine/)
29. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 581 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
30. [**Reducing Transformer Depth on Demand with Structured Dropout**Angela Fan, Edouard Grave, Armand Joulin*2019 · 546 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
31. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 536 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)
32. [**Regularizing Neural Networks by Penalizing Confident Output Distributions**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 482 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
33. [**PPT: Pre-trained Prompt Tuning for Few-shot Learning**Yuxian Gu, Xu Han, Zhiyuan Liu, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/gu-2022-ppt-pre-trained-prompt-tuning/)
34. [**Masked Autoencoders Are Scalable Vision Learners**Kaiming He, Xinlei Chen, Saining Xie, et al.*2021 · 463 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/)
35. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 459 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
36. [**LinkBERT: Pretraining Language Models with Document Links**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/)
37. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework**Peng Wang, An Yang, Rui Men, et al.*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
38. [**Masked Language Model Scoring**Julian Salazar, Davis Liang, Toan Q. Nguyen, et al.*2020 · 444 citations*](https://md.research.elamur.ai/free/en/papers/salazar-2020-masked-language-model-scoring/)
39. [**COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task**Ricardo Rei, José G. C. de Souza, Duarte Alves, et al.*2022 · 433 citations*](https://md.research.elamur.ai/free/en/papers/rei-2022-comet-22-unbabel-ist-2022/)
40. [**Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics**Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov*2021 · 417 citations*](https://md.research.elamur.ai/free/en/papers/pagnoni-2021-understanding-factuality-abstractive-summarization-frank/)
41. [**InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks**Zhe Chen, Jiannan Wu, Wenhai Wang, et al.*2023 · 409 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
42. [**Automatic Detection of Generated Text is Easiest when Humans are Fooled**Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, et al.*2019 · 406 citations*](https://md.research.elamur.ai/free/en/papers/ippolito-2019-automatic-detection-generated-text-easiest/)
43. [**Training data-efficient image transformers & distillation through attention**Hugo Touvron, Matthieu Cord, Matthijs Douze, et al.*2020 · 405 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2020-training-data-efficient-image-transformers/)
44. [**A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios**Michael A. Hedderich, Lukas Lange, Heike Adel, et al.*2021 · 399 citations*](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
45. [**Revisiting Few-sample BERT Fine-tuning**Tianyi Zhang, Felix Wu, Arzoo Katiyar, et al.*2020 · 361 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-revisiting-few-sample-bert-fine/)
46. [**Adversarial Training for Unsupervised Bilingual Lexicon Induction**Meng Zhang, Yang Liu, Huanbo Luan, et al.*2017 · 359 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-adversarial-training-unsupervised-bilingual-lexicon/)
47. [**BRIO: Bringing Order to Abstractive Summarization**Yixin Liu, Pengfei Liu, Dragomir Radev, et al.*2022 · 357 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-brio-bringing-order-abstractive-summarization/)
48. [**Adversarial Removal of Demographic Attributes from Text Data**Yanai Elazar, Yoav Goldberg*2018 · 357 citations*](https://md.research.elamur.ai/free/en/papers/elazar-2018-adversarial-removal-demographic-attributes-text/)
49. [**Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor**Or Honovich, Thomas Scialom, Omer Levy, et al.*2023 · 356 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2023-unnatural-instructions-tuning-language-models/)
50. [**Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback**Baolin Peng, Michel Galley, Pengcheng He, et al.*2023 · 354 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/)
51. [**QuestEval: Summarization Asks for Fact-based Evaluation**Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.*2021 · 353 citations*](https://md.research.elamur.ai/free/en/papers/scialom-2021-questeval-summarization-asks-fact-based/)
52. [**ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate**Chi-Min Chan, Weize Chen, Yusheng Su, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/chan-2023-chateval-better-llm-based/)
53. [**FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization**Esin Durmus, He He, Mona Diab*2020 · 350 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/)
54. [**SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization**Yixin Liu, Pengfei Liu*2021 · 340 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-simcls-simple-framework-contrastive-learning/)
55. [**LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models**Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, et al.*2023 · 335 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-llmlingua-compressing-prompts-accelerated-inference/)
56. [**RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 327 citations*](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/)
57. [**How many data points is a prompt worth?**Teven Le Scao, Alexander Rush*2021 · 315 citations*](https://md.research.elamur.ai/free/en/papers/scao-2021-how-many-data-points-is/)
58. [**LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion**Dongfu Jiang, Xiang Ren, Bill Yuchen Lin*2023 · 314 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/)
59. [**Abductive Commonsense Reasoning**Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, et al.*2019 · 301 citations*](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)
60. [**Challenges and Applications of Large Language Models**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
61. [**Evaluating Verifiability in Generative Search Engines**Nelson Liu, Tianyi Zhang, Percy Liang*2023 · 294 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/)
62. [**FlauBERT: Unsupervised Language Model Pre-training for French**Hang Le, Loïc Vial, Jibril Frej, et al.*2019 · 293 citations*](https://md.research.elamur.ai/free/en/papers/le-2019-flaubert-unsupervised-language-model-pre/)
63. [**Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 292 citations*](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/)
64. [**QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization**Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, et al.*2022 · 288 citations*](https://md.research.elamur.ai/free/en/papers/fabbri-2022-qafacteval-improved-qa-based-factual/)
65. [**USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation**Shikib Mehri, Maxine Eskenazi*2020 · 283 citations*](https://md.research.elamur.ai/free/en/papers/mehri-2020-usr-unsupervised-reference-free-evaluation/)
66. [**Is ChatGPT a Good NLG Evaluator? A Preliminary Study**Jiaan Wang, Yunlong Liang, Fandong Meng, et al.*2023 · 281 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/)
67. [**AlignScore: Evaluating Factual Consistency with A Unified Alignment Function**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 280 citations*](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/)
68. [**Ragas: Automated Evaluation of Retrieval Augmented Generation**Shahul Es, Jithin James, Luis Espinosa-Anke, et al.*2023 · 280 citations*](https://md.research.elamur.ai/free/en/papers/es-2023-ragas-automated-evaluation-retrieval-augmented/)
69. [**TRUE: Re-evaluating Factual Consistency Evaluation**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 276 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/)
70. [**XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond**Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados*2022 · 271 citations*](https://md.research.elamur.ai/free/en/papers/barbieri-2022-xlm-t-multilingual-language-models/)
71. [**Prometheus: Inducing Fine-grained Evaluation Capability in Language Models**Seungone Kim, Jamin Shin, Yejin Cho, et al.*2023 · 267 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-prometheus-inducing-fine-grained-evaluation/)
72. [**Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust**Markus Freitag, Ricardo Rei, Nitika Mathur, et al.*2022 · 263 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2022-results-wmt22-metrics-shared-task/)
73. [**Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing**Brian Thompson, Matt Post*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/thompson-2020-automatic-machine-translation-evaluation-many/)
74. [**Re-evaluating Evaluation in Text Summarization**Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, et al.*2020 · 256 citations*](https://md.research.elamur.ai/free/en/papers/bhandari-2020-re-evaluating-evaluation-text-summarization/)
75. [**Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks**Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, et al.*2021 · 248 citations*](https://md.research.elamur.ai/free/en/papers/mahabadi-2021-parameter-efficient-multi-task-fine/)
76. [**Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 248 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
77. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 243 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
78. [**Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports**Yuhao Zhang, Derek Merck, Emily Tsai, et al.*2020 · 240 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-optimizing-factual-correctness-summary-study/)
79. [**LaMP: When Large Language Models Meet Personalization**Alireza Salemi, Sheshera Mysore, Michael Bendersky, et al.*2024 · 239 citations*](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/)
80. [**Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training**Oshin Agarwal, Heming Ge, Siamak Shakeri, et al.*2021 · 238 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2021-knowledge-graph-based-synthetic-corpus/)
81. [**COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs**Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, et al.*2020 · 235 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/)
82. [**Evaluating Open-Domain Question Answering in the Era of Large Language Models**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/)
83. [**Detecting Hallucinated Content in Conditional Neural Sequence Generation**Chunting Zhou, Graham Neubig, Jiatao Gu, et al.*2021 · 229 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2021-detecting-hallucinated-content-conditional-neural/)
84. [**Neural CRF Model for Sentence Alignment in Text Simplification**Chao Jiang, Mounica Maddela, Wuwei Lan, et al.*2020 · 227 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-neural-crf-model-sentence-alignment/)
85. [**BLEU might be Guilty but References are not Innocent**Markus Freitag, David Grangier, Isaac Caswell*2020 · 223 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2020-bleu-might-be-guilty-but/)
86. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 220 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
87. [**Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing**Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, et al.*2019 · 217 citations*](https://md.research.elamur.ai/free/en/papers/ponti-2019-modeling-language-variation-universals-survey/)
88. [**Distilling Reasoning Capabilities into Smaller Language Models**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/)
89. [**YiSi - a Unified Semantic MT Quality Evaluation and Estimation Metric for Languages with Different Levels of Available Resources**Chi-kiu Lo*2019 · 216 citations*](https://md.research.elamur.ai/free/en/papers/lo-2019-yisi-unified-semantic-mt-quality/)
90. [**Compressing Context to Enhance Inference Efficiency of Large Language Models**Yucheng Li, Bo Dong, Frank Guerin, et al.*2023 · 208 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-compressing-context-enhance-inference-efficiency/)
91. [**LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models**Yen-Ting Lin, Yun-Nung Chen*2023 · 206 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/)
92. [**Towards a Unified Multi-Dimensional Evaluator for Text Generation**Ming Zhong, Yang Liu, Da Yin, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2022-unified-multi-dimensional-evaluator-text/)
93. [**On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation**Ruidan He, Linlin Liu, Hai Ye, et al.*2021 · 204 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/)
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