---
title: "Who cites Prasetya Ajie Utama"
description: "294 papers of the Elamur Research library cite the works of Prasetya Ajie Utama, found in their bibliographies; the most cited first."
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---
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# Who cites Prasetya Ajie Utama

295 citations of their works from 294 papers of our library (of the 140,884 whose bibliographies we read) · 43 with a page here, the most cited first · [← Prasetya Ajie Utama](https://md.research.elamur.ai/free/en/authors/prasetya-ajie-utama/)

1. [**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/)
2. [**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/)
3. [**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/)
4. [**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/)
5. [**A Survey on Automated Fact-Checking**Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)
6. [**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/)
7. [**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/)
8. [**Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence**Tal Schuster, Adam Fisch, Regina Barzilay*2021 · 289 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/)
9. [**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/)
10. [**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/)
11. [**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/)
12. [**CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization**Shuyang Cao, Lu Wang*2021 · 257 citations*](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/)
13. [**Hallucinations in Large Multilingual Translation Models**Nuno M. Guerreiro, Duarte M. Alves, Jonas Waldendorf, et al.*2023 · 244 citations*](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-hallucinations-large-multilingual-translation-models/)
14. [**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/)
15. [**Factual Error Correction for Abstractive Summarization Models**Meng Cao, Yue Dong, Jiapeng Wu, et al.*2020 · 217 citations*](https://md.research.elamur.ai/free/en/papers/cao-2020-factual-error-correction-abstractive-summarization/)
16. [**Annotating and Modeling Fine-grained Factuality in Summarization**Tanya Goyal, Greg Durrett*2021 · 215 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2021-annotating-modeling-fine-grained-factuality/)
17. [**RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 204 citations*](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/)
18. [**Evaluating Factuality in Generation with Dependency-level Entailment**Tanya Goyal, Greg Durrett*2020 · 201 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2020-evaluating-factuality-generation-dependency-level/)
19. [**Challenges in Automated Debiasing for Toxic Language Detection**Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, et al.*2021 · 190 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2021-challenges-automated-debiasing-toxic-language/)
20. [**Planning with Learned Entity Prompts for Abstractive Summarization**Shashi Narayan, Yao Zhao, Joshua Maynez, et al.*2021 · 180 citations*](https://md.research.elamur.ai/free/en/papers/narayan-2021-planning-learned-entity-prompts-abstractive/)
21. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
22. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
23. [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/)
24. [**Investigating Pretrained Language Models for Graph-to-Text Generation**Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, et al.*2021 · 149 citations*](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/)
25. [**A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation**Tianyu Liu, Yizhe Zhang, Chris Brockett, et al.*2022 · 140 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/)
26. [**A fine-grained comparison of pragmatic language understanding in humans and language models**Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
27. [**FLUTE: Figurative Language Understanding through Textual Explanations**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/)
28. [**Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors**Liyan Tang, Tanya Goyal, Alex Fabbri, et al.*2023 · 126 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/)
29. [**LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization**Kalpesh Krishna, Erin Bransom, Bailey Kuehl, et al.*2023 · 112 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2023-longeval-guidelines-human-evaluation-faithfulness/)
30. [**Metaphor Understanding Challenge Dataset for LLMs**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/)
31. [**A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models**Francesca De Luca Fornaciari, Begoña Altuna, Itziar Gonzalez-Dios, et al.*2024 · 22 citations*](https://md.research.elamur.ai/free/en/papers/fornaciari-2024-hard-nut-crack-idiom-detection/)
32. [**Robustifying Multi-hop QA through Pseudo-Evidentiality Training**Kyungjae Lee, Seung-won Hwang, Sang-eun Han, et al.*2021 · 19 citations*](https://md.research.elamur.ai/free/en/papers/lee-2021-robustifying-multi-hop-qa-through/)
33. [**IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions**Ziheng Zeng, Kellen Tan Cheng, Srihari Venkat Nanniyur, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2023-iekg-commonsense-knowledge-graph-idiomatic/)
34. [**Coreference Reasoning in Machine Reading Comprehension**Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, et al.*2021 · 11 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-coreference-reasoning-machine-reading-comprehension/)
35. [**SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection**Jianhua Yuan, Yanyan Zhao, Yanyue Lu, et al.*2022 · 6 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2022-ssr-utilizing-simplified-stance-reasoning/)
36. [**No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language Models**Kellen Tan Cheng, Suma Bhat*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2024-no-context-needed-contextual-quandary/)
37. [**ITUNLP at MWE-2026 AdMIRe 2: A Zero-Shot LLM Pipeline for Multimodal Idiom Understanding and Ranking**Atakan Site, Oğuz Ali Arslan, Gülşen Eryiğit*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/site-2026-itunlp-mwe-2026-admire-2/)
38. [**SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech**Johan Nevin Sofalas, Dilushri Pavithra, Nevidu Jayatilleke, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/sofalas-2026-sinfos-parallel-dataset-translating-sinhala/)
39. [**DRInQ: Evaluating Conversational Implicature with Controlled Context Variation**Hirona Jacqueline Arai, Xiang Ren*2026*](https://md.research.elamur.ai/free/en/papers/arai-2026-drinq-evaluating-conversational-implicature-controlled/)
40. [**Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb–Object Constructions**Prisca Piccirilli, Alexander Fraser, Sabine Schulte im Walde*2026*](https://md.research.elamur.ai/free/en/papers/piccirilli-2026-floating-or-suggesting-ideas-large/)
41. [**I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memes**Shijia Zhou, Saif M. Mohammad, Barbara Plank, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhou-2026-i-came-i-saw-i/)
42. [**Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning**Maggie Mi, Golzar Atefi, Atsuki Yamaguchi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mi-2026-rethinking-idiomaticity-decomposability-hypothesis-evidence/)
43. [**Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/)

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

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