---
title: "Who cites Vidhisha Balachandran — 556 papers of our library"
description: "556 papers of the Elamur Research library cite the works of Vidhisha Balachandran, found in their bibliographies; the most cited first."
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---
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# Who cites Vidhisha Balachandran

593 citations of their works from 556 papers of our library (of the 141,840 whose bibliographies we read) · 46 with a page here, the most cited first · [← Vidhisha Balachandran](https://md.research.elamur.ai/free/en/authors/vidhisha-balachandran/)

1. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,957 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 · 3,222 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
3. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,703 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
4. [**Bias and Fairness in Large Language Models: A Survey**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,074 citations*](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/)
5. [**FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation**Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/)
6. [**Unleashing the potential of prompt engineering for large language models**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
7. [**The Internal State of an LLM Knows When It's Lying**Amos Azaria, Tom Mitchell*2023 · 427 citations*](https://md.research.elamur.ai/free/en/papers/azaria-2023-internal-state-llm-knows-when/)
8. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 379 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/)
9. [**Teaching Small Language Models to Reason**Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, et al.*2023 · 340 citations*](https://md.research.elamur.ai/free/en/papers/magister-2023-teaching-small-language-models-reason/)
10. [**From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models**Shangbin Feng, Chan Young Park, Yuhan Liu, et al.*2023 · 306 citations*](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/)
11. [**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/)
12. [**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/)
13. [**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/)
14. [**CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization**Shuyang Cao, Lu Wang*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/)
15. [**Trusting Your Evidence: Hallucinate Less with Context-aware Decoding**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/)
16. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 207 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
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. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
19. [**Knowledge Conflicts for LLMs: A Survey**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 179 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/)
20. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 178 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
21. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
22. [**Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization**Meng Cao, Yue Dong, Jackie Cheung*2022 · 167 citations*](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/)
23. [**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/)
24. [**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/)
25. [**When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs**Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/)
26. [**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/)
27. [**Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization**Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, et al.*2023 · 119 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-models-are-not/)
28. [**OpenThoughts: Data Recipes for Reasoning Models**Etash Guha, Ryan Marten, Sedrick Keh, et al.*2025 · 113 citations*](https://md.research.elamur.ai/free/en/papers/guha-2025-openthoughts-data-recipes-reasoning-models/)
29. [**Olmo 3**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
30. [**ExpertQA: Expert-Curated Questions and Attributed Answers**Chaitanya Malaviya, Subin Lee, Sihao Chen, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/)
31. [**WiCE: Real-World Entailment for Claims in Wikipedia**Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/kamoi-2023-wice-real-world-entailment-claims/)
32. [**TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models**Zorik Gekhman, Jonathan Herzig, Roee Aharoni, et al.*2023 · 106 citations*](https://md.research.elamur.ai/free/en/papers/gekhman-2023-trueteacher-learning-factual-consistency-evaluation/)
33. [**FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization**David Wan, Mohit Bansal*2022 · 101 citations*](https://md.research.elamur.ai/free/en/papers/wan-2022-factpegasus-factuality-aware-pre-training/)
34. [**Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark**Nouha Dziri, Hannah Rashkin, Tal Linzen, et al.*2022 · 99 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2022-evaluating-attribution-dialogue-systems-begin/)
35. [**Fine-grained Hallucination Detection and Editing for Language Models**Abhika Mishra, Akari Asai, Vidhisha Balachandran, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2024-fine-grained-hallucination-detection-editing/)
36. [**WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild**Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-wildbench-benchmarking-llms-challenging-tasks/)
37. [**FineSurE: Fine-grained Summarization Evaluation using LLMs**Hwanjun Song, Hang Su, Igor Shalyminov, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/song-2024-finesure-fine-grained-summarization-evaluation/)
38. [**Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey**Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/)
39. [**Generating Benchmarks for Factuality Evaluation of Language Models**Dor Muhlgay, Ori Ram, Inbal Magar, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/muhlgay-2024-generating-benchmarks-factuality-evaluation-language/)
40. [**MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning**Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, et al.*2024 · 70 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-mediq-question-asking-llms-benchmark/)
41. [**RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework**Yifan Wang, Vera Demberg*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-rsa-control-pragmatics-grounded-lightweight/)
42. [**Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries**Valeria Pastorino, Nafise Sadat Moosavi*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/pastorino-2026-frame-frame-out-measuring-framing/)
43. [**HalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification**Salah Eddine Bekhouche; Abdessalam Bouchekif; Hichem Telli; Mohammed-En-Nadhir Zighem;, Abdenour Hadid*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/bekhouche-2026-hallutruthqa-4k-fine-grained-corpus/)
44. [**Detecting Hallucinations in Authentic LLM–Human Interactions**Yujie Ren, Niklas Gruhlke, Anne Lauscher*2026*](https://md.research.elamur.ai/free/en/papers/ren-2026-detecting-hallucinations-authentic-llm-human/)
45. [**HotelCheckSpan: A Benchmark Dataset for LLM Faithfulness**Patricia Schmidtova, Ondrej Dusek, Saad Mahamood*2026*](https://md.research.elamur.ai/free/en/papers/schmidtova-2026-hotelcheckspan-benchmark-dataset-llm-faithfulness/)
46. [**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/)

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

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