--- title: "Who cites Karishma Mandyam — 82 papers of our library" description: "82 papers of the Elamur Research library cite the works of Karishma Mandyam, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/karishma-mandyam/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/karishma-mandyam/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Karishma Mandyam](https://md.research.elamur.ai/free/en/authors/karishma-mandyam/) / cited by # Who cites Karishma Mandyam 82 citations of their works from 82 papers of our library (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← Karishma Mandyam](https://md.research.elamur.ai/free/en/authors/karishma-mandyam/) 1. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations* 2. **[Query Rewriting in Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 384 citations* 3. **[TimeLMs: Diachronic Language Models from Twitter](https://md.research.elamur.ai/free/en/papers/loureiro-2022-timelms-diachronic-language-models-twitter/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Loureiro, Francesco Barbieri, Leonardo Neves, et al.*2022 · 331 citations* 4. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations* 5. **[RealTime QA: What's the Answer Right Now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.*2022 · 181 citations* 6. [**Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts arXiv:2305.13300**Jian Xie, Kai Zhang, Jiangjie Chen, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-adaptive-chameleon-stubborn-sloth-revealing/) 7. [**Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models arXiv:2208.03306**Margaret Li, Suchin Gururangan, Tim Dettmers, et al.*2022 · 114 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-branch-train-merge-embarrassingly-parallel/) 8. [**A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/) 9. **[R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'](https://md.research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hanning Zhang, Shizhe Diao, Yong Lin, et al.*2024 · 99 citations* 10. [**Tuning Language Models by Proxy arXiv:2401.08565**Alisa Liu, Xiaochuang Han, Yizhong Wang, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-tuning-language-models-proxy/) 11. [**Kimi K2.5: Visual Agentic Intelligence arXiv:2602.02276**Kimi Team, Tongtong Bai, Yifan Bai, et al.*2026 · 66 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2026-kimi-k2-5-visual/) 12. **[From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms](https://md.research.elamur.ai/free/en/papers/luo-2026-storage-experience-survey-evolution-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinghao Luo, Yuchen Tian, Chuxue Cao, et al.*2026 · 3 citations* 13. **[When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/ortu-2026-when-seeing-overrides-knowing-disentangling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Francesco Ortu, Zhijing Jin, Diego Doimo, et al.*2026 · 2 citations* 14. **[SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility](https://md.research.elamur.ai/free/en/papers/zhi-2026-spard-self-paced-curriculum-rl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuyang Zhi, Peilun Zhou, Chengqiang Lu, et al.*2026 · 1 citation* 15. **[Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies?](https://md.research.elamur.ai/free/en/papers/zhu-2026-can-large-language-models-effectively/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mengna Zhu, Jibing Wu, Lihua Liu, et al.*2026* 16. **[Complex-IF and Beyond: Expert Rubrics for RLVR](https://md.research.elamur.ai/free/en/papers/mehta-2026-complex-if-beyond-expert-rubrics/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Sushant Mehta, Liudas Panavas, Eleanor Fleming, et al.*2026* 17. **[Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation](https://md.research.elamur.ai/free/en/papers/yang-2026-data-efficient-adaptation-contextual-shifts/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hyeongjun Yang, Donghyun Kim, Seokju Hwang, et al.*2026* 18. **[Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering](https://md.research.elamur.ai/free/en/papers/nishida-2026-dissecting-graphrag-modular-analysis-knowledge/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Noriki Nishida, Rumana Ferdous Munne, Shanshan Liu, et al.*2026* 19. **[Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance](https://md.research.elamur.ai/free/en/papers/seegmiller-2026-measuring-distribution-shift-user-prompts/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Parker Seegmiller, Sarah Masud Preum*2026* 63 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/karishma-mandyam/) — Karishma Mandyam