🧑‍🔬 Who cites Rahul Jha

281 citations of their works from 277 papers of our library (of the 141,840 whose bibliographies we read) · 34 with a page here, the most cited first · ← Rahul Jha

Survey of Hallucination in Natural Language GenerationACM Computing SurveysZiwei Ji, Nayeon Lee, Rita Frieske, et al.2022 · 5,957 citations
TruthfulQA: Measuring How Models Mimic Human FalsehoodsAnnual Meeting of the Association for Computational Linguistics (ACL)Stephanie Lin, Jacob Hilton, Owain Evans2022 · 2,052 citations
1. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsarXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5BvosjAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,703 citations
2. Retrieval-Augmented Generation for Large Language Models: A SurveyarXiv:2312.10997Yunfan Gao, Yun Xiong, Xinyu Gao, et al.2023 · 1,648 citations
   Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language ModelsComputational LinguisticsYue Zhang, Yafu Li, Leyang Cui, et al.2023 · 966 citations
3. LongBench: A Bilingual, Multitask Benchmark for Long Context UnderstandingarXiv:2308.14508Yushi Bai, Xin Lv, Jiajie Zhang, et al.2023 · 506 citations
   SnapKV: LLM Knows What You are Looking for Before GenerationConference on Neural Information Processing Systems (NeurIPS)Yuhong Li, Yingbing Huang, Bowen Yang, et al.2024 · 350 citations
4. UL2: Unifying Language Learning ParadigmsarXiv:2205.05131Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.2022 · 335 citations
   QAFactEval: Improved QA-Based Factual Consistency Evaluation for SummarizationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, et al.2022 · 288 citations
   AlignScore: Evaluating Factual Consistency with A Unified Alignment FunctionAnnual Meeting of the Association for Computational Linguistics (ACL)Yuheng Zha, Yichi Yang, Ruichen Li, et al.2023 · 280 citations
   TRUE: Re-evaluating Factual Consistency EvaluationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Or Honovich, Roee Aharoni, Jonathan Herzig, et al.2022 · 276 citations
   CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive SummarizationConference on Empirical Methods in Natural Language Processing (EMNLP)Shuyang Cao, Lu Wang2021 · 258 citations
   ASQA: Factoid Questions Meet Long-Form AnswersConference on Empirical Methods in Natural Language Processing (EMNLP)Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, et al.2022 · 239 citations
   RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Cheng Niu, Yuanhao Wu, Juno Zhu, et al.2024 · 204 citations
   A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark DatasetsFindings of the Association for Computational LinguisticsMd Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.2023 · 200 citations
   Planning with Learned Entity Prompts for Abstractive SummarizationTransactions of the Association for Computational Linguistics (TACL)Shashi Narayan, Yao Zhao, Joshua Maynez, et al.2021 · 180 citations
5. Effective Long-Context Scaling of Foundation ModelsarXiv:2309.16039Wenhan Xiong, Jingyu Liu, Igor Molybog, et al.2023 · 177 citations
   SCROLLS: Standardized CompaRison Over Long Language SequencesConference on Empirical Methods in Natural Language Processing (EMNLP)Uri Shaham, Elad Segal, Maor Ivgi, et al.2022 · 162 citations
6. L-Eval: Instituting Standardized Evaluation for Long Context Language ModelsarXiv:2307.11088Chenxin An, Shansan Gong, Ming Zhong, et al.2023 · 160 citations
7. Orca 2: Teaching Small Language Models How to ReasonarXiv:2311.11045Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.2023 · 160 citations
8. PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information FunnelingarXiv:2406.02069Zefan Cai, Yichi Zhang, Bofei Gao, et al.2024 · 151 citations
9. Exploring the Limits of ChatGPT for Query or Aspect-based Text SummarizationarXiv:2302.08081Xianjun Yang, Yan Li, Xinlu Zhang, et al.2023 · 141 citations
   SummScreen: A Dataset for Abstractive Screenplay SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL)Mingda Chen, Zewei Chu, Sam Wiseman, et al.2022 · 136 citations
10. LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context MultitasksarXiv:2412.15204Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.2024 · 130 citations
11. Retrieval meets Long Context Large Language ModelsarXiv:2310.03025Peng Xu, Wei Ping, Xianchao Wu, et al.2023 · 129 citations
    ZeroSCROLLS: A Zero-Shot Benchmark for Long Text UnderstandingFindings of the Association for Computational LinguisticsUri Shaham, Maor Ivgi, Avia Efrat, et al.2023 · 129 citations
    A Length-Extrapolatable TransformerAnnual Meeting of the Association for Computational Linguistics (ACL)Yutao Sun, Li Dong, Barun Patra, et al.2023 · 117 citations
12. Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human EvaluationarXiv:2212.07981Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.2022 · 116 citations
    Evaluating Attribution in Dialogue Systems: The BEGIN BenchmarkTransactions of the Association for Computational Linguistics (TACL)Nouha Dziri, Hannah Rashkin, Tal Linzen, et al.2022 · 99 citations
    Efficient Long-Text Understanding with Short-Text ModelsTransactions of the Association for Computational Linguistics (TACL)Maor Ivgi, Uri Shaham, Jonathan Berant2023 · 93 citations
    CoLT5: Faster Long-Range Transformers with Conditional ComputationConference on Empirical Methods in Natural Language Processing (EMNLP)Joshua Ainslie, Tao Lei, Michiel de Jong, et al.2023 · 83 citations
    BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language ModelsLanguage Resources and Evaluation Conference (LREC)Zican Dong, Tianyi Tang, Junyi Li, et al.2024 · 59 citations
    InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in SummarizationLanguage Resources and Evaluation Conference (LREC)Kosuke Nishida, Kyosuke Nishida, Itsumi Saito2026
    Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)Journal of SoftwareZeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.2024

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