---
title: "Who cites Melanie Subbiah — page 5 — 11,345 citing papers"
description: "11,345 papers of the Elamur Research library cite the works of Melanie Subbiah, found in their bibliographies · page 5 of 20 · 401–500; the…"
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---
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# Who cites Melanie Subbiah

11,345 citations of their works from 11,345 papers of our library (of the 149,439 whose bibliographies we read) · 1,982 with a page here, the most cited first · page 5 of 20 · 401–500 · [← Melanie Subbiah](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/)

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401. [**Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection arXiv:2302.12173**Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/)
402. **[LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2022-lexglue-benchmark-dataset-legal-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Abhik Jana, Dirk Hartung, et al.*2022 · 239 citations*
403. **[FUDGE: Controlled Text Generation With Future Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kevin Yang, Dan Klein*2021 · 239 citations*
404. [**COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs**Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, et al.*2020 · 239 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/)
405. **[Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger](https://md.research.elamur.ai/free/en/papers/qi-2021-hidden-killer-invisible-textual-backdoor/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fanchao Qi, Mukai Li, Yangyi Chen, et al.*2021 · 238 citations*
406. **[The Importance of Modeling Social Factors of Language: Theory and Practice](https://md.research.elamur.ai/free/en/papers/hovy-2021-importance-modeling-social-factors-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dirk Hovy, Diyi Yang*2021 · 238 citations*
407. [**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/)
408. **[Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2023-principle-driven-self-alignment-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Yikang Shen, Qinhong Zhou, et al.*2023 · 236 citations*
409. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
410. **[Detecting and Preventing Hallucinations in Large Vision Language Models](https://md.research.elamur.ai/free/en/papers/gunjal-2023-detecting-preventing-hallucinations-large-vision/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Anisha Gunjal, Jihan Yin, Erhan Bas*2023 · 235 citations*
411. **[Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://md.research.elamur.ai/free/en/papers/sui-2023-table-meets-llm-large-language/) [ACM International Conference on Web Search and Data Mining (WSDM)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-web-search-and-data-mining-wsdm/)**Yuan Sui, Mengyu Zhou, Mingjie Zhou, et al.*2023 · 235 citations*
412. **[Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/schick-2020-automatically-identifying-words-that-can/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Timo Schick, Helmut Schmid, Hinrich Schütze*2020 · 234 citations*
413. **[Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations*
414. [**Steering Language Models With Activation Engineering arXiv:2308.10248**Alexander Matt Turner, Lisa Thiergart, Gavin Leech, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/turner-2023-steering-language-models-activation-engineering/)
415. **[Hurdles to Progress in Long-form Question Answering](https://md.research.elamur.ai/free/en/papers/krishna-2021-hurdles-progress-long-form-question/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kalpesh Krishna, Aurko Roy, Mohit Iyyer*2021 · 233 citations*
416. **[MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?](https://md.research.elamur.ai/free/en/papers/zhang-2024-mathverse-multi-modal-llm-truly/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Renrui Zhang, Dongzhi Jiang, Yichi Zhang, et al.*2024 · 232 citations*
417. [**GAIA: a benchmark for General AI Assistants arXiv:2311.12983**Gregoire Mialon, Clementine Fourrier, Craig Swift, et al.*2023 · 232 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-gaia-benchmark-general-ai-assistants/)
418. **[Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://md.research.elamur.ai/free/en/papers/zhou-2021-detecting-hallucinated-content-conditional-neural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunting Zhou, Graham Neubig, Jiatao Gu, et al.*2021 · 232 citations*
419. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning arXiv:2304.06588**Petter Tornberg*2023 · 231 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/)
420. [**LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model arXiv:2304.15010**Peng Gao, Jiaming Han, Renrui Zhang, et al.*2023 · 229 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-llama-adapter-v2-parameter-efficient/)
421. [**Progress measures for grokking via mechanistic interpretability arXiv:2301.05217**Neel Nanda, Lawrence Chan, Tom Lieberum, et al.*2023 · 228 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/)
422. [**Self-critiquing models for assisting human evaluators arXiv:2206.05802**William Saunders, Catherine Yeh, Jeff Wu, et al.*2022 · 228 citations*](https://md.research.elamur.ai/free/en/papers/saunders-2022-self-critiquing-models-assisting-human/)
423. [**Qwen2.5-Omni Technical Report arXiv:2503.20215**Jin Xu, Zhifang Guo, Jinzheng He, et al.*2025 · 227 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen2-5-omni-technical/)
424. [**MedAlpaca — An Open-Source Collection of Medical Conversational AI Models and Training Data arXiv:2304.08247**Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, et al.*2023 · 227 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-medalpaca-open-source-collection-medical/)
425. [**A Survey of Hallucination in Large Foundation Models arXiv:2309.05922**Vipula Rawte, Amit Sheth, Amitava Das*2023 · 226 citations*](https://md.research.elamur.ai/free/en/papers/rawte-2023-survey-hallucination-large-foundation-models/)
426. **[DataComp: In search of the next generation of multimodal datasets](https://md.research.elamur.ai/free/en/papers/gadre-2023-datacomp-search-next-generation-multimodal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al.*2023 · 224 citations*
427. [**Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting arXiv:2305.07004**Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/)
428. [**Reinforced Self-Training (ReST) for Language Modeling arXiv:2308.08998**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
429. [**Specializing Smaller Language Models towards Multi-Step Reasoning arXiv:2301.12726**Yao Fu, Hao Peng, Litu Ou, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-specializing-smaller-language-models-towards/)
430. **[GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information](https://md.research.elamur.ai/free/en/papers/jin-2023-genegpt-augmenting-large-language-models/) [Bioinformatics](https://md.research.elamur.ai/free/en/venues/bioinformatics/)**Qiao Jin, Yifan Yang, Qingyu Chen, et al.*2023 · 223 citations*
431. **[Self-planning Code Generation with Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2023-self-planning-code-generation-large/) [ACM Transactions on Software Engineering and Methodology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-software-engineering-and-methodology/)**Xue Jiang, Yihong Dong, Lecheng Wang, et al.*2023 · 223 citations*
432. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
433. **[Transformer Memory as a Differentiable Search Index](https://md.research.elamur.ai/free/en/papers/tay-2022-transformer-memory-differentiable-search-index/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yi Tay, Vinh Q. Tran, Mostafa Dehghani, et al.*2022 · 223 citations*
434. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models arXiv:2405.17428**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 222 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
435. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP arXiv:2212.14024**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 222 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
436. **[Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://md.research.elamur.ai/free/en/papers/pal-2023-med-halt-medical-domain-hallucination/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu*2023 · 221 citations*
437. **[Multilingual Translation from Denoising Pre-Training](https://md.research.elamur.ai/free/en/papers/tang-2021-multilingual-translation-denoising-pre-training/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuqing Tang, Chau Tran, Xian Li, et al.*2021 · 220 citations*
438. **[Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 219 citations*
439. [**Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning arXiv:2209.14610**Pan Lu, Liang Qiu, Kai-Wei Chang, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/)
440. [**FOLIO: Natural Language Reasoning with First-Order Logic arXiv:2209.00840**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
441. [**Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models arXiv:2308.16149**Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, et al.*2023 · 218 citations*](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/)
442. **[TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://md.research.elamur.ai/free/en/papers/hu-2023-tifa-accurate-interpretable-text-image/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Yushi Hu, Benlin Liu, Jungo Kasai, et al.*2023 · 218 citations*
443. [**Teaching language models to support answers with verified quotes arXiv:2203.11147**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 218 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
444. [**Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4 arXiv:2304.03439**Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/)
445. **[Making Language Models Better Reasoners with Step-Aware Verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 217 citations*
446. [**Safe RLHF: Safe Reinforcement Learning from Human Feedback arXiv:2310.12773**Josef Dai, Xuehai Pan, Ruiyang Sun, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-safe-rlhf-safe-reinforcement-learning/)
447. [**LLM+P: Empowering Large Language Models with Optimal Planning Proficiency arXiv:2304.11477**Bo Liu, Yuqian Jiang, Xiaohan Zhang, et al.*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-llm-p-empowering-large-language/)
448. **[Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models](https://md.research.elamur.ai/free/en/papers/iv-2022-cutting-down-prompts-parameters-simple/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Robert Logan IV, Ivana Balazevic, Eric Wallace, et al.*2022 · 216 citations*
449. **[MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://md.research.elamur.ai/free/en/papers/kudugunta-2023-madlad-400-multilingual-document-level/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sneha Kudugunta, Isaac Caswell, Biao Zhang, et al.*2023 · 213 citations*
450. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 213 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
451. [**Demystifying Prompts in Language Models via Perplexity Estimation arXiv:2212.04037**Hila Gonen, Srini Iyer, Terra Blevins, et al.*2022 · 213 citations*](https://md.research.elamur.ai/free/en/papers/gonen-2022-demystifying-prompts-language-models-perplexity/)
452. **[Challenges in Detoxifying Language Models](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 213 citations*
453. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP arXiv:2103.00453**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 213 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
454. [**Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation arXiv:2401.08417**Haoran Xu, Amr Sharaf, Yunmo Chen, et al.*2024 · 212 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-contrastive-preference-optimization-pushing-boundaries/)
455. **[What matters when building vision-language models?](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2024-matters-building-vision-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.*2024 · 212 citations*
456. **[Compressing Context to Enhance Inference Efficiency of Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-compressing-context-enhance-inference-efficiency/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yucheng Li, Bo Dong, Frank Guerin, et al.*2023 · 212 citations*
457. **[Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 212 citations*
458. [**Ring Attention with Blockwise Transformers for Near-Infinite Context arXiv:2310.01889**Hao Liu, Matei Zaharia, Pieter Abbeel*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-ring-attention-blockwise-transformers-near/)
459. **[Sources of Hallucination by Large Language Models on Inference Tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 212 citations*
460. **[Language Models of Code are Few-Shot Commonsense Learners](https://md.research.elamur.ai/free/en/papers/madaan-2022-language-models-code-few-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Aman Madaan, Shuyan Zhou, Uri Alon, et al.*2022 · 212 citations*
461. [**Scalable Extraction of Training Data from (Production) Language Models arXiv:2311.17035**Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/nasr-2023-scalable-extraction-training-data-production/)
462. **[“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters](https://md.research.elamur.ai/free/en/papers/wan-2023-kelly-is-warm-person-joseph/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixin Wan, George Pu, Jiao Sun, et al.*2023 · 211 citations*
463. **[An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nicholas Meade, Elinor Poole-Dayan, Siva Reddy*2022 · 211 citations*
464. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding arXiv:2202.04538**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
465. [**Prompting GPT-3 To Be Reliable arXiv:2210.09150**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
466. **[CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://md.research.elamur.ai/free/en/papers/ye-2021-crossfit-few-shot-learning-challenge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qinyuan Ye, Bill Yuchen Lin, Xiang Ren*2021 · 210 citations*
467. [**SALMONN: Towards Generic Hearing Abilities for Large Language Models arXiv:2310.13289**Changli Tang, Wenyi Yu, Guangzhi Sun, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-salmonn-towards-generic-hearing-abilities/)
468. **[Active Example Selection for In-Context Learning](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yiming Zhang, Shi Feng, Chenhao Tan*2022 · 209 citations*
469. [**Internet-augmented language models through few-shot prompting for open-domain question answering arXiv:2203.05115**Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/)
470. **[What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Garg, Dimitris Tsipras, Percy Liang, et al.*2022 · 209 citations*
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473. [**Recursively Summarizing Books with Human Feedback arXiv:2109.10862**Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.*2021 · 208 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-recursively-summarizing-books-human-feedback/)
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476. **[RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 207 citations*
477. [**LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models arXiv:2305.13711**Yen-Ting Lin, Yun-Nung Chen*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/)
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479. **[Woodpecker: Hallucination Correction for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/yin-2023-woodpecker-hallucination-correction-multimodal-large/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Shukang Yin, Chaoyou Fu, Sirui Zhao, et al.*2023 · 206 citations*
480. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
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484. [**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/)
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487. [**Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs arXiv:2310.01801**Suyu Ge, Yunan Zhang, Liyuan Liu, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-model-tells-discard-adaptive-kv/)
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500. [**MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action arXiv:2303.11381**Zhengyuan Yang, Linjie Li, Jianfeng Wang, et al.*2023 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-mm-react-prompting-chatgpt-multimodal/)

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