---
title: "Who cites SAMSum Corpus: A — 380 citing papers"
description: "380 papers of the Elamur Research library cite «SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization»…"
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---
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# Who cites «SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization»

380 papers of our library cite it (of the 149,439 whose bibliographies we read) · 60 with a page here, the most cited first · [← back to «SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization»](https://md.research.elamur.ai/free/en/papers/gliwa-2019-samsum-corpus-human-annotated-dialogue/)

1. [**LoRA: Low-Rank Adaptation of Large Language Models arXiv:2106.09685**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 8,661 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
2. **[Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,974 citations*
3. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
4. [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,612 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
5. [**A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity arXiv:2302.04023**Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.*2023 · 882 citations*](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
6. **[Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 828 citations*
7. [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding arXiv:2308.14508**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 514 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/)
8. [**UL2: Unifying Language Learning Paradigms arXiv:2205.05131**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 347 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
9. **[SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 306 citations*
10. **[DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://md.research.elamur.ai/free/en/papers/chen-2021-dialogsum-real-life-scenario-dialogue/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yulong Chen, Yang Liu, Liang Chen, et al.*2021 · 301 citations*
11. **[AlignScore: Evaluating Factual Consistency with A Unified Alignment Function](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 284 citations*
12. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model arXiv:2402.07827**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 253 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
13. **[Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/chen-2020-multi-view-sequence-sequence-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaao Chen, Diyi Yang*2020 · 204 citations*
14. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
15. **[MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/zhu-2021-mediasum-large-scale-media-interview/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chenguang Zhu, Yang Liu, Jie Mei, et al.*2021 · 181 citations*
16. **[Planning with Learned Entity Prompts for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/narayan-2021-planning-learned-entity-prompts-abstractive/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shashi Narayan, Yao Zhao, Joshua Maynez, et al.*2021 · 180 citations*
17. **[Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://md.research.elamur.ai/free/en/papers/balloccu-2024-leak-cheat-repeat-data-contamination/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, et al.*2024 · 174 citations*
18. [**PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling arXiv:2406.02069**Zefan Cai, Yichi Zhang, Bofei Gao, et al.*2024 · 155 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-pyramidkv-dynamic-kv-cache-compression/)
19. [**Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization arXiv:2302.08081**Xianjun Yang, Yan Li, Xinlu Zhang, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-exploring-limits-chatgpt-query-aspect/)
20. **[SummScreen: A Dataset for Abstractive Screenplay Summarization](https://md.research.elamur.ai/free/en/papers/chen-2022-summscreen-dataset-abstractive-screenplay-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingda Chen, Zewei Chu, Sam Wiseman, et al.*2022 · 136 citations*
21. **[Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs](https://md.research.elamur.ai/free/en/papers/chen-2021-structure-aware-abstractive-conversation-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jiaao Chen, Diyi Yang*2021 · 135 citations*
22. **[GO FIGURE: A Meta Evaluation of Factuality in Summarization](https://md.research.elamur.ai/free/en/papers/gabriel-2021-go-figure-meta-evaluation-factuality/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, et al.*2021 · 130 citations*
23. [**Time Travel in LLMs: Tracing Data Contamination in Large Language Models arXiv:2308.08493**Shahriar Golchin, Mihai Surdeanu*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/golchin-2023-time-travel-llms-tracing-data/)
24. **[Unlearn What You Want to Forget: Efficient Unlearning for LLMs](https://md.research.elamur.ai/free/en/papers/chen-2023-unlearn-what-you-want-forget/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaao Chen, Diyi Yang*2023 · 124 citations*
25. [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation arXiv:2212.07981**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
26. [**DistiLLM: Towards Streamlined Distillation for Large Language Models arXiv:2402.03898**Jongwoo Ko, Sungnyun Kim, Tianyi Chen, et al.*2024 · 112 citations*](https://md.research.elamur.ai/free/en/papers/ko-2024-distillm-towards-streamlined-distillation-large/)
27. **[A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://md.research.elamur.ai/free/en/papers/laskar-2024-systematic-survey-critical-review-evaluating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.*2024 · 105 citations*
28. [**Large Language Models are Inconsistent and Biased Evaluators arXiv:2405.01724**Rickard Stureborg, Dimitris Alikaniotis, Yoshi Suhara*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/stureborg-2024-large-language-models-inconsistent-biased/)
29. **[FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-followbench-multi-level-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Xingshan Zeng, et al.*2024 · 86 citations*
30. **[Multilingual Large Language Models Are Not (Yet) Code-Switchers](https://md.research.elamur.ai/free/en/papers/zhang-2023-multilingual-large-language-models-are/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, et al.*2023 · 86 citations*
31. **[SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2024-seaeval-multilingual-foundation-models-cross/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Zhengyuan Liu, Xin Huang, et al.*2024 · 85 citations*
32. **[In-Context Learning with Long-Context Models: An In-Depth Exploration](https://md.research.elamur.ai/free/en/papers/bertsch-2025-context-learning-long-context-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Amanda Bertsch, Maor Ivgi, Emily Xiao, et al.*2025 · 81 citations*
33. **[Coreference-Aware Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/liu-2021-coreference-aware-dialogue-summarization/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Zhengyuan Liu, Ke Shi, Nancy Chen*2021 · 81 citations*
34. **[Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems](https://md.research.elamur.ai/free/en/papers/laban-2024-summary-haystack-challenge-long-context/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Philippe Laban, Alexander Fabbri, Caiming Xiong, et al.*2024 · 69 citations*
35. **[Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue](https://md.research.elamur.ai/free/en/papers/gu-2024-model-editing-harms-general-abilities/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, et al.*2024 · 67 citations*
36. **[On Context Utilization in Summarization with Large Language Models](https://md.research.elamur.ai/free/en/papers/ravaut-2024-context-utilization-summarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mathieu Ravaut, Aixin Sun, Nancy Chen, et al.*2024 · 67 citations*
37. **[Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://md.research.elamur.ai/free/en/papers/li-2024-leveraging-large-language-models-nlg/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Li, Xiaohan Xu, Tao Shen, et al.*2024 · 63 citations*
38. [**Exploring the Benefits of Training Expert Language Models over Instruction Tuning arXiv:2302.03202**Joel Jang, Seungone Kim, Seonghyeon Ye, et al.*2023 · 63 citations*](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/)
39. **[Hello Again! LLM-powered Personalized Agent for Long-term Dialogue](https://md.research.elamur.ai/free/en/papers/li-2025-hello-again-llm-powered-personalized/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hao Li, Chenghao Yang, An Zhang, et al.*2025 · 55 citations*
40. **[A Systematic Survey of Automatic Prompt Optimization Techniques](https://md.research.elamur.ai/free/en/papers/ramnath-2025-systematic-survey-automatic-prompt-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kiran Ramnath, Kang Zhou, Sheng Guan, et al.*2025 · 40 citations*
41. **[DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://md.research.elamur.ai/free/en/papers/zhang-2023-dialogstudio-towards-richest-most-diverse/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianguo Zhang, Kun Qian, Zhiwei Liu, et al.*2023 · 21 citations*
42. **[Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-token-level-density-based-uncertainty/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, et al.*2025 · 13 citations*
43. **[Understanding LLM Reasoning for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/yuan-2026-understanding-llm-reasoning-abstractive-summarization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haohan Yuan, Haopeng Zhang*2026 · 11 citations*
44. **[Evaluating Dialect Robustness of Language Models via Conversation Understanding](https://md.research.elamur.ai/free/en/papers/srirag-2025-evaluating-dialect-robustness-language-models/) [Workshop on Scaling Up Multilingual Evaluation](https://md.research.elamur.ai/free/en/venues/sumeval/)**Dipankar Srirag, Nihar Ranjan Sahoo, Aditya Joshi*2025 · 10 citations*
45. **[Factual Dialogue Summarization via Learning from Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2025-factual-dialogue-summarization-learning-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Rongxin Zhu, Jey Han Lau, Jianzhong Qi*2025 · 10 citations*
46. **[Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-unconditional-truthfulness-learning-unconditional-uncertainty/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, et al.*2025 · 10 citations*
47. **[ZERA: Zero-init Instruction Evolving Refinement Agent – From Zero Instructions to Structured Prompts via Principle-based Optimization](https://md.research.elamur.ai/free/en/papers/yi-2025-zera-zero-init-instruction-evolving/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seungyoun Yi, Minsoo Khang, Sungrae Park*2025 · 5 citations*
48. **[MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application](https://md.research.elamur.ai/free/en/papers/peng-2026-multifinben-benchmarking-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xueqing Peng, Lingfei Qian, Yan Wang, et al.*2026 · 4 citations*
49. **[Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets](https://md.research.elamur.ai/free/en/papers/hsiung-2026-why-llm-safety-guardrails-collapse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lei Hsiung, Tianyu Pang, Yung-Chen Tang, et al.*2026 · 4 citations*
50. [**Less is More: Selective Reflection for Compatible and Efficient Knowledge Distillation in Large Language Models arXiv:2508.06135**Lingyuan Liu, Mengxiang Zhang*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-less-more-selective-reflection-compatible/)
51. **[MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation](https://md.research.elamur.ai/free/en/papers/singh-2026-mt-osc-path-llms-that/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jyotika Singh, Fang Tu, Miguel Ballesteros, et al.*2026 · 3 citations*
52. **[Harnessing Consistency for Robust Test-Time LLM Ensemble](https://md.research.elamur.ai/free/en/papers/zeng-2026-harnessing-consistency-robust-test-time/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichen Zeng, Qi Yu, Xiao Lin, et al.*2026 · 2 citations*
53. **[K-Merge: Online Continual Merging of Adapters for On-device Large Language Models](https://md.research.elamur.ai/free/en/papers/shenaj-2026-k-merge-online-continual-merging/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Donald Shenaj, Ondrej Bohdal, Taha Ceritli, et al.*2026 · 2 citations*
54. **[An Analysis of Dialogue Act Sequence Similarity Across Multiple Domains](https://md.research.elamur.ai/free/en/papers/enayet-2022-analysis-dialogue-act-sequence-similarity/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ayesha Enayet, Gita Sukthankar*2022 · 2 citations*
55. **[Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution](https://md.research.elamur.ai/free/en/papers/zhou-2026-deputy-accelerating-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuhua Zhou, Shichao Weng, Changhai Zhou, et al.*2026 · 1 citation*
56. **[LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams](https://md.research.elamur.ai/free/en/papers/wu-2026-livelongbench-tackling-long-context-understanding/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yongxuan Wu, Runyu Chen, Peiyu Liu, et al.*2026 · 1 citation*
57. **[What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search](https://md.research.elamur.ai/free/en/papers/zhang-2026-what-makes-llm-good-optimizer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinhao Zhang, Xi Chen, François Portet, et al.*2026 · 1 citation*
58. **[What Matters to an LLM? Behavioral and Computational Evidences from Summarization](https://md.research.elamur.ai/free/en/papers/zhou-2026-what-matters-llm-behavioral-computational/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yongxin Zhou, Changshun Wu, Philippe Mulhem, et al.*2026 · 1 citation*
59. **[Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/mei-2026-beyond-overlap-metrics-rewarding-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoyong Mei, Tingting Zuo, Da Chen, et al.*2026*
60. **[Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing](https://md.research.elamur.ai/free/en/papers/wang-2026-federated-lora-fine-tuning-pipelined/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoran Wang, Xiong Wang, Yuqing Li, et al.*2026*

320 more papers of our library cite it; they have no page here yet.

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