---
title: "Who cites Akhila Yerukola — 339 papers of our library"
description: "339 papers of the Elamur Research library cite the works of Akhila Yerukola, found in their bibliographies; the most cited first."
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---
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# Who cites Akhila Yerukola

342 citations of their works from 339 papers of our library (of the 149,439 whose bibliographies we read) · 75 with a page here, the most cited first · [← Akhila Yerukola](https://md.research.elamur.ai/free/en/authors/akhila-yerukola/)

1. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
2. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
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. **[On Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/maynez-2020-faithfulness-factuality-abstractive-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joshua Maynez, Shashi Narayan, Bernd Bohnet, et al.*2020 · 1,857 citations*
5. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
6. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
7. **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,082 citations*
8. [**GLM-130B: An Open Bilingual Pre-trained Model arXiv:2210.02414**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 786 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
9. [**AgentBench: Evaluating LLMs as Agents arXiv:2308.03688**Xiao Liu, Hao Yu, Hanchen Zhang, et al.*2023 · 432 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-agentbench-evaluating-llms-agents/)
10. **[CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://md.research.elamur.ai/free/en/papers/lee-2022-coauthor-designing-human-ai-collaborative/) [CHI Conference on Human Factors in Computing Systems](https://md.research.elamur.ai/free/en/venues/chi-conference-on-human-factors-in-computing-systems/)**Mina Lee, Percy Liang, Qian Yang*2022 · 423 citations*
11. **[Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://md.research.elamur.ai/free/en/papers/ippolito-2019-automatic-detection-generated-text-easiest/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, et al.*2019 · 411 citations*
12. [**Evaluation of Text Generation: A Survey arXiv:2006.14799**Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao*2020 · 388 citations*](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2020-evaluation-text-generation-survey/)
13. [**Datasets: A Community Library for Natural Language Processing arXiv:2109.02846**Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, et al.*2021 · 368 citations*](https://md.research.elamur.ai/free/en/papers/lhoest-2021-datasets-community-library-natural-language/)
14. [**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/)
15. **[Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://md.research.elamur.ai/free/en/papers/krishna-2023-paraphrasing-evades-detectors-ai-generated/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kalpesh Krishna, Yixiao Song, Marzena Karpinska, et al.*2023 · 310 citations*
16. **[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*
17. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
18. **[TRUE: Re-evaluating Factual Consistency Evaluation](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 279 citations*
19. **[LaMP: When Large Language Models Meet Personalization](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alireza Salemi, Sheshera Mysore, Michael Bendersky, et al.*2024 · 246 citations*
20. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
21. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models arXiv:2405.01535**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 234 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
22. **[Enabling Language Models to Fill in the Blanks](https://md.research.elamur.ai/free/en/papers/donahue-2020-enabling-language-models-fill-blanks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Donahue, Mina Lee, Percy Liang*2020 · 209 citations*
23. [**mGPT: Few-Shot Learners Go Multilingual arXiv:2204.07580**Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, et al.*2022 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shliazhko-2022-mgpt-few-shot-learners-go/)
24. **[Measuring Attribution in Natural Language Generation Models](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, et al.*2023 · 201 citations*
25. **[AraT5: Text-to-Text Transformers for Arabic Language Generation](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed*2022 · 201 citations*
26. **[ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://md.research.elamur.ai/free/en/papers/cheng-2025-elephant-measuring-understanding-social-sycophancy/) [Science](https://md.research.elamur.ai/free/en/venues/science/)**Myra Cheng, Sunny Yu, Cinoo Lee, et al.*2025 · 188 citations*
27. **[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 182 citations*
28. **[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*
29. **[“I'm sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset](https://md.research.elamur.ai/free/en/papers/smith-2022-i-m-sorry-hear-that/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eric Michael Smith, Melissa Hall, Melanie Kambadur, et al.*2022 · 167 citations*
30. **[Locally Typical Sampling](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Clara Meister, Tiago Pimentel, Gian Wiher, et al.*2023 · 157 citations*
31. **[LLM-based NLG Evaluation: Current Status and Challenges](https://md.research.elamur.ai/free/en/papers/gao-2025-llm-based-nlg-evaluation-current/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Mingqi Gao, Xinyu Hu, Xunjian Yin, et al.*2025 · 153 citations*
32. **[A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Liu, Yizhe Zhang, Chris Brockett, et al.*2022 · 142 citations*
33. **[TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://md.research.elamur.ai/free/en/papers/uchendu-2021-turingbench-benchmark-environment-turing-test/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adaku Uchendu, Zeyu Ma, Thai Le, et al.*2021 · 140 citations*
34. **[ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://md.research.elamur.ai/free/en/papers/koto-2024-arabicmmlu-assessing-massive-multitask-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fajri Koto, Haonan Li, Sara Shatnawi, et al.*2024 · 117 citations*
35. [**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/)
36. [**ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning arXiv:2111.10952**Vamsi Aribandi, Yi Tay, Tal Schuster, et al.*2021 · 114 citations*](https://md.research.elamur.ai/free/en/papers/aribandi-2021-ext5-towards-extreme-multi-task/)
37. **[Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://md.research.elamur.ai/free/en/papers/aliannejadi-2021-building-evaluating-open-domain-dialogue/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, et al.*2021 · 111 citations*
38. **[Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation](https://md.research.elamur.ai/free/en/papers/deng-2021-compression-transduction-creation-unified-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Bowen Tan, Zhengzhong Liu, et al.*2021 · 102 citations*
39. **[Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark](https://md.research.elamur.ai/free/en/papers/dziri-2022-evaluating-attribution-dialogue-systems-begin/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nouha Dziri, Hannah Rashkin, Tal Linzen, et al.*2022 · 100 citations*
40. **[Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation](https://md.research.elamur.ai/free/en/papers/bulian-2022-tomayto-tomahto-beyond-token-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jannis Bulian, Christian Buck, Wojciech Gajewski, et al.*2022 · 100 citations*
41. **[On Learning to Summarize with Large Language Models as References](https://md.research.elamur.ai/free/en/papers/liu-2024-learning-summarize-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yixin Liu, Kejian Shi, Katherine He, et al.*2024 · 94 citations*
42. **[NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/) [Northern European Journal of Language Technology](https://md.research.elamur.ai/free/en/venues/northern-european-journal-of-language-technology/)**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 86 citations*
43. [**AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model arXiv:2208.01448**Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.*2022 · 84 citations*](https://md.research.elamur.ai/free/en/papers/soltan-2022-alexatm-20b-few-shot-learning/)
44. **[ORCA: A Challenging Benchmark for Arabic Language Understanding](https://md.research.elamur.ai/free/en/papers/elmadany-2023-orca-challenging-benchmark-arabic-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**AbdelRahim Elmadany, ElMoatez Billah Nagoudi, Muhammad Abdul-Mageed*2023 · 83 citations*
45. [**LLMs Get Lost In Multi-Turn Conversation arXiv:2505.06120**Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, et al.*2025 · 82 citations*](https://md.research.elamur.ai/free/en/papers/laban-2025-llms-get-lost-multi-turn/)
46. [**Does Writing with Language Models Reduce Content Diversity? arXiv:2309.05196**Vishakh Padmakumar, He He*2023 · 82 citations*](https://md.research.elamur.ai/free/en/papers/padmakumar-2023-writing-language-models-reduce-content/)
47. [**Benchmarking Benchmark Leakage in Large Language Models arXiv:2404.18824**Ruijie Xu, Zengzhi Wang, Run-Ze Fan, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmarking-benchmark-leakage-large-language/)
48. **[AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs](https://md.research.elamur.ai/free/en/papers/mousi-2025-aradice-benchmarks-dialectal-cultural-capabilities/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Basel Mousi, Nadir Durrani, Fatema Ahmad, et al.*2025 · 68 citations*
49. **[Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy](https://md.research.elamur.ai/free/en/papers/ippolito-2023-preventing-generation-verbatim-memorization-language/) [International Natural Language Generation Conference (INLG)](https://md.research.elamur.ai/free/en/venues/inlg/)**Daphne Ippolito, Florian Tramer, Milad Nasr, et al.*2023 · 68 citations*
50. **[SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://md.research.elamur.ai/free/en/papers/ye-2023-satlm-satisfiability-aided-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Qiaochu Chen, Isil Dillig, et al.*2023 · 62 citations*
51. [**Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity**Juri Opitz, Angel Daza, Anette Frank*2021 · 47 citations*](https://md.research.elamur.ai/free/en/papers/opitz-2021-weisfeiler-leman-bamboo-novel-amr/)
52. **[SLING: Sino Linguistic Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2022-sling-sino-linguistic-evaluation-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixiao Song, Kalpesh Krishna, Rajesh Bhatt, et al.*2022 · 41 citations*
53. **[Dolphin: A Challenging and Diverse Benchmark for Arabic NLG](https://md.research.elamur.ai/free/en/papers/nagoudi-2023-dolphin-challenging-diverse-benchmark-arabic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**El Moatez Billah Nagoudi, AbdelRahim Elmadany, Ahmed Oumar El-Shangiti, et al.*2023 · 35 citations*
54. **[Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://md.research.elamur.ai/free/en/papers/zhou-2025-culture-is-not-trivia-sociocultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Naitian Zhou, David Bamman, Isaac L. Bleaman*2025 · 33 citations*
55. **[Assessing Discourse Relations in Language Generation from GPT-2](https://md.research.elamur.ai/free/en/papers/ko-2020-assessing-discourse-relations-language-generation/) [International Natural Language Generation Conference (INLG)](https://md.research.elamur.ai/free/en/venues/inlg/)**Wei-Jen Ko, Junyi Jessy Li*2020 · 30 citations*
56. [**PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning arXiv:2305.19472**Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/brahman-2023-plasma-making-small-language-models/)
57. **[Suri: Multi-constraint Instruction Following in Long-form Text Generation](https://md.research.elamur.ai/free/en/papers/pham-2024-suri-multi-constraint-instruction-following/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chau Minh Pham, Simeng Sun, Mohit Iyyer*2024 · 19 citations*
58. **[AAVENUE: Detecting LLM Biases on NLU Tasks in AAVE via a Novel Benchmark](https://md.research.elamur.ai/free/en/papers/gupta-2024-aavenue-detecting-llm-biases-nlu/) [Workshop on NLP for Positive Impact](https://md.research.elamur.ai/free/en/venues/nlp4pi/)**Abhay Gupta, Ece Yurtseven, Philip Meng, et al.*2024 · 17 citations*
59. **[LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://md.research.elamur.ai/free/en/papers/s-2024-lexsumm-lext5-benchmarking-modeling-legal/) [Natural Legal Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/nllp/)**Santosh T.Y.S.S, Cornelius Weiss, Matthias Grabmair*2024 · 16 citations*
60. **[Benchmarking LLaMA-3 on Arabic Language Generation Tasks](https://md.research.elamur.ai/free/en/papers/khondaker-2024-benchmarking-llama-3-arabic-language/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Md Tawkat Islam Khondaker, Numaan Naeem, Fatimah Khan, et al.*2024 · 12 citations*
61. **[Multiple LLM Agents Debate for Equitable Cultural Alignment](https://md.research.elamur.ai/free/en/papers/ki-2025-multiple-llm-agents-debate-equitable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dayeon Ki, Rachel Rudinger, Tianyi Zhou, et al.*2025 · 10 citations*
62. **[Scaling Collaborative Effort with Agents](https://md.research.elamur.ai/free/en/papers/shen-2026-scaling-collaborative-effort-agents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shannon Zejiang Shen, Valerie Chen, Ken Gu, et al.*2026 · 7 citations*
63. **[What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts](https://md.research.elamur.ai/free/en/papers/yang-2026-what-prompts-don-t-say/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenyang Yang, Yike Shi, Qianou Ma, et al.*2026 · 6 citations*
64. [**Benchmarking Frontier LLMs on Arabic Cultural and Sociolinguistic Knowledge: A Cross-Evaluation Framework with Human SME Ground Truth arXiv:2607.00139**Sajjad Abdoli, Ghassan Al-Sumaidaee, Ahmad ElShiekh, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/abdoli-2026-benchmarking-frontier-llms-arabic-cultural/)
65. **[Deciphering Cultural Representations in Large Language Models via Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/zou-2026-deciphering-cultural-representations-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenye Zou, Difan Jiao, Lijie Hu*2026 · 1 citation*
66. **[MarsadLab at PalmX Shared Task: An LLM Benchmark for Arabic Culture and Islamic Civilization](https://md.research.elamur.ai/free/en/papers/biswas-2025-marsadlab-palmx-shared-task-llm/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Md. Rafiul Biswas, Shimaa Ibrahim, Kais Attia, et al.*2025 · 1 citation*
67. **[AI Safety Lost in Translation: Evaluating the Effectiveness of English-Italian Cross-Lingual LLM Safety Alignment](https://md.research.elamur.ai/free/en/papers/wu-2026-ai-safety-lost-translation-evaluating/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Alessio Wu, Martim Brandao*2026*
68. **[Common to Whom? Regional Cultural Commonsense and LLM Bias in India](https://md.research.elamur.ai/free/en/papers/madhusudan-2026-common-whom-regional-cultural-commonsense/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno, et al.*2026*
69. **[DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI](https://md.research.elamur.ai/free/en/papers/zhang-2026-defgen-bench-benchmark-chinese-criminal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Senbo Zhang, Qiqi Wang, Fanghao Lou, et al.*2026*
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