---
title: "Who cites Christopher E. Potts — page 7 — 9,352 citing papers"
description: "9,352 papers of the Elamur Research library cite the works of Christopher E. Among them: The Geometry of Truth: Emergent Linear Structure…"
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---
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# Who cites Christopher E. Potts

11,943 citations of their works from 9,352 papers of our library (of the 149,439 whose bibliographies we read) · 1,871 with a page here, the most cited first · page 7 of 19 · 601–700 · [← Christopher E. Potts](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/)

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601. [**The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets arXiv:2310.06824**Samuel Marks, Max Tegmark*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/marks-2023-geometry-truth-emergent-linear-structure/)
602. [**GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher arXiv:2308.06463**Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-gpt-4-too-smart-safe/)
603. **[“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*
604. **[Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction](https://md.research.elamur.ai/free/en/papers/sainz-2021-label-verbalization-entailment-effective-zero/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, et al.*2021 · 167 citations*
605. **[MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 166 citations*
606. **[The Life and Death of Discourse Entities: Identifying Singleton Mentions](https://md.research.elamur.ai/free/en/papers/recasens-2013-life-death-discourse-entities-identifying/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Marta Recasens, Marie-Catherine de Marneffe, Christopher Potts*2013 · 166 citations*
607. **[Language Models as Agent Models](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jacob Andreas*2022 · 165 citations*
608. **[What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in Web argumentation](https://md.research.elamur.ai/free/en/papers/habernal-2016-what-makes-convincing-argument-empirical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ivan Habernal, Iryna Gurevych*2016 · 165 citations*
609. **[AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2022-americasnli-evaluating-zero-shot-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, et al.*2022 · 164 citations*
610. **[FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts](https://md.research.elamur.ai/free/en/papers/gong-2023-figstep-jailbreaking-large-vision-language/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yichen Gong, Delong Ran, Jinyuan Liu, et al.*2023 · 163 citations*
611. **[Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation](https://md.research.elamur.ai/free/en/papers/huang-2019-achieving-verified-robustness-symbol-substitutions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Po-Sen Huang, Robert Stanforth, Johannes Welbl, et al.*2019 · 163 citations*
612. [**Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue**Shereen Oraby, Vrindavan Harrison, Lena Reed, et al.*2016 · 163 citations*](https://md.research.elamur.ai/free/en/papers/oraby-2016-creating-characterizing-diverse-corpus-sarcasm/)
613. [**Orca 2: Teaching Small Language Models How to Reason arXiv:2311.11045**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
614. [**Language models show human-like content effects on reasoning tasks arXiv:2207.07051**Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)
615. [**Compositionality decomposed: how do neural networks generalise? arXiv:1908.08351**Dieuwke Hupkes, Verna Dankers, Mathijs Mul, et al.*2019 · 162 citations*](https://md.research.elamur.ai/free/en/papers/hupkes-2019-compositionality-decomposed-neural-networks-generalise/)
616. **[Deep Learning Models for Sentiment Analysis in Arabic](https://md.research.elamur.ai/free/en/papers/sallab-2015-deep-learning-models-sentiment-analysis/) [Workshop on Arabic Natural Language Processing](https://md.research.elamur.ai/free/en/venues/workshop-on-arabic-natural-language-processing/)**Ahmad Al Sallab, Hazem Hajj, Gilbert Badaro, et al.*2015 · 162 citations*
617. **[LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.*2024 · 161 citations*
618. [**Reasoning with Language Model Prompting: A Survey arXiv:2212.09597**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
619. **[Larger-Scale Transformers for Multilingual Masked Language Modeling](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Naman Goyal, Jingfei Du, Myle Ott, et al.*2021 · 161 citations*
620. [**Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition**Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/jeretic-2020-are-natural-language-inference-models/)
621. **[Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhang-2015-shallow-convolutional-neural-network-implicit/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Biao Zhang, Jinsong Su, Deyi Xiong, et al.*2015 · 161 citations*
622. **[Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://md.research.elamur.ai/free/en/papers/lehman-2019-inferring-which-medical-treatments-work/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Eric Lehman, Jay DeYoung, Regina Barzilay, et al.*2019 · 160 citations*
623. **[Specialising Word Vectors for Lexical Entailment](https://md.research.elamur.ai/free/en/papers/vulic-2018-specialising-word-vectors-lexical-entailment/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ivan Vulić, Nikola Mrkšić*2018 · 160 citations*
624. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
625. [**Jamba: A Hybrid Transformer-Mamba Language Model arXiv:2403.19887**Opher Lieber, Barak Lenz, Hofit Bata, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/lieber-2024-jamba-hybrid-transformer-mamba-language/)
626. **[Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering](https://md.research.elamur.ai/free/en/papers/wu-2022-self-adaptive-context-learning-information/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, et al.*2022 · 159 citations*
627. **[Understanding tables with intermediate pre-training](https://md.research.elamur.ai/free/en/papers/eisenschlos-2020-understanding-tables-intermediate-pre-training/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Julian Eisenschlos, Syrine Krichene, Thomas Müller*2020 · 158 citations*
628. [**Leveraging Large Language Models for Multiple Choice Question Answering arXiv:2210.12353**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 157 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
629. **[Reducing Quantity Hallucinations in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/zhao-2020-reducing-quantity-hallucinations-abstractive-summarization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheng Zhao, Shay B. Cohen, Bonnie Webber*2020 · 157 citations*
630. **[ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 156 citations*
631. **[Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://md.research.elamur.ai/free/en/papers/monroe-2017-colors-context-pragmatic-neural-model/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, et al.*2017 · 156 citations*
632. [**ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning arXiv:2212.07919**Olga Golovneva, Moya Chen, Spencer Poff, et al.*2022 · 155 citations*](https://md.research.elamur.ai/free/en/papers/golovneva-2022-roscoe-suite-metrics-scoring-step/)
633. **[Explaining NLP Models via Minimal Contrastive Editing (MiCE)](https://md.research.elamur.ai/free/en/papers/ross-2021-explaining-nlp-models-minimal-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexis Ross, Ana Marasović, Matthew Peters*2021 · 155 citations*
634. [**ART: Automatic multi-step reasoning and tool-use for large language models arXiv:2303.09014**Bhargavi Paranjape, Scott Lundberg, Sameer Singh, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/paranjape-2023-art-automatic-multi-step-reasoning/)
635. [**Ask Me Anything: A simple strategy for prompting language models arXiv:2210.02441**Simran Arora, Avanika Narayan, Mayee F. Chen, et al.*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/arora-2022-ask-me-anything-simple-strategy/)
636. **[Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/angelidis-2018-multiple-instance-learning-networks-fine/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Stefanos Angelidis, Mirella Lapata*2018 · 154 citations*
637. **[Fine-Tuning Language Models with Just Forward Passes](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sadhika Malladi, Tianyu Gao, Eshaan Nichani, et al.*2023 · 153 citations*
638. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs arXiv:2503.01743**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 152 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
639. [**AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators arXiv:2303.16854**Xingwei He, Zhenghao Lin, Yeyun Gong, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/)
640. [**Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation arXiv:2305.15852**Niels Mundler, Jingxuan He, Slobodan Jenko, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/mundler-2023-self-contradictory-hallucinations-large-language/)
641. **[How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 151 citations*
642. [**Unsolved Problems in ML Safety arXiv:2109.13916**Dan Hendrycks, Nicholas Carlini, John Schulman, et al.*2021 · 151 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-unsolved-problems-ml-safety/)
643. **[Reevaluating Adversarial Examples in Natural Language](https://md.research.elamur.ai/free/en/papers/morris-2020-reevaluating-adversarial-examples-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**John Morris, Eli Lifland, Jack Lanchantin, et al.*2020 · 151 citations*
644. [**A Review on Language Models as Knowledge Bases arXiv:2204.06031**Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2022-review-language-models-knowledge-bases/)
645. **[INFOTABS: Inference on Tables as Semi-structured Data](https://md.research.elamur.ai/free/en/papers/gupta-2020-infotabs-inference-tables-as-semi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, et al.*2020 · 150 citations*
646. **[Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/dou-2019-investigating-meta-learning-algorithms-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos*2019 · 150 citations*
647. **[Discourse Parsing with Attention-based Hierarchical Neural Networks](https://md.research.elamur.ai/free/en/papers/li-2016-discourse-parsing-attention-based-hierarchical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qi Li, Tianshi Li, Baobao Chang*2016 · 150 citations*
648. **[MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages](https://md.research.elamur.ai/free/en/papers/zhang-2023-miracl-multilingual-retrieval-dataset-covering/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, et al.*2023 · 149 citations*
649. **[Self-Evaluation Guided Beam Search for Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 149 citations*
650. [**MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge arXiv:2206.08853**Linxi Fan, Guanzhi Wang, Yunfan Jiang, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/fan-2022-minedojo-building-open-ended-embodied/)
651. **[BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://md.research.elamur.ai/free/en/papers/song-2021-bob-bert-over-bert-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoyu Song, Yan Wang, Kaiyan Zhang, et al.*2021 · 149 citations*
652. **[Do latent tree learning models identify meaningful structure in sentences?](https://md.research.elamur.ai/free/en/papers/williams-2018-do-latent-tree-learning-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Adina Williams, Andrew Drozdov, Samuel R. Bowman*2018 · 149 citations*
653. **[Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 148 citations*
654. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations*
655. **[Context-faithful Prompting for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 148 citations*
656. **[Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://md.research.elamur.ai/free/en/papers/honovich-2022-instruction-induction-few-examples-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Or Honovich, Uri Shaham, Samuel R. Bowman, et al.*2022 · 148 citations*
657. **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations*
658. **[Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?](https://md.research.elamur.ai/free/en/papers/pham-2021-out-order-how-important-is/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Thang Pham, Trung Bui, Long Mai, et al.*2021 · 148 citations*
659. [**OCNLI: Original Chinese Natural Language Inference arXiv:2010.05444**Hai Hu, Kyle Richardson, Liang Xu, et al.*2020 · 148 citations*](https://md.research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/)
660. **[HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment](https://md.research.elamur.ai/free/en/papers/vulic-2017-hyperlex-large-scale-evaluation-graded/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Ivan Vulić, Daniela Gerz, Douwe Kiela, et al.*2017 · 148 citations*
661. **[Varieties of update](https://md.research.elamur.ai/free/en/papers/murray-2014-varieties-update/) [Semantics and Pragmatics](https://md.research.elamur.ai/free/en/venues/semantics-and-pragmatics/)**Sarah E Murray*2014 · 148 citations*
662. [**MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation arXiv:2310.03302**Qian Huang, Jian Vora, Percy Liang, et al.*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-mlagentbench-evaluating-language-agents/)
663. [**Is GPT-3 a Good Data Annotator? arXiv:2212.10450**Bosheng Ding, Chengwei Qin, Linlin Liu, et al.*2022 · 147 citations*](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/)
664. **[Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders](https://md.research.elamur.ai/free/en/papers/liu-2021-fast-effective-self-supervised-transforming/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fangyu Liu, Ivan Vulić, Anna Korhonen, et al.*2021 · 147 citations*
665. **[A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/hanselowski-2019-richly-annotated-corpus-different-tasks/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Andreas Hanselowski, Christian Stab, Claudia Schulz, et al.*2019 · 147 citations*
666. **[Unified Demonstration Retriever for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaonan Li, Kai Lv, Hang Yan, et al.*2023 · 146 citations*
667. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
668. **[RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder](https://md.research.elamur.ai/free/en/papers/xiao-2022-retromae-pre-training-retrieval-oriented/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shitao Xiao, Zheng Liu, Yingxia Shao, et al.*2022 · 145 citations*
669. **[Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://md.research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Chung, Ece Kamar, Saleema Amershi*2023 · 144 citations*
670. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
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672. **[Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://md.research.elamur.ai/free/en/papers/wang-2022-iteratively-prompt-pre-trained-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Boshi Wang, Xiang Deng, Huan Sun*2022 · 144 citations*
673. **[From Arguments to Key Points: Towards Automatic Argument Summarization](https://md.research.elamur.ai/free/en/papers/haim-2020-arguments-key-points-automatic-argument/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roy Bar-Haim, Lilach Eden, Roni Friedman, et al.*2020 · 144 citations*
674. [**A Roadmap to Pluralistic Alignment arXiv:2402.05070**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
675. [**DeepInception: Hypnotize Large Language Model to Be Jailbreaker arXiv:2311.03191**Xuan Li, Zhanke Zhou, Jianing Zhu, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/)
676. **[Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://md.research.elamur.ai/free/en/papers/wen-2023-hard-prompts-made-easy-gradient/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxin Wen, Neel Jain, John Kirchenbauer, et al.*2023 · 141 citations*
677. **[Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/bartolo-2020-beat-ai-investigating-adversarial-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Max Bartolo, Alastair Roberts, Johannes Welbl, et al.*2020 · 141 citations*
678. **[RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/shimanaka-2018-ruse-regressor-sentence-embeddings-automatic/) [Conference on Machine Translation: Shared Task Papers](https://md.research.elamur.ai/free/en/venues/conference-on-machine-translation-shared-task-papers/)**Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi*2018 · 141 citations*
679. **[A computational approach to politeness with application to social factors](https://md.research.elamur.ai/free/en/papers/mizil-2013-computational-approach-politeness-application-social/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, et al.*2013 · 141 citations*
680. **[Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step](https://md.research.elamur.ai/free/en/papers/li-2023-symbolic-chain-thought-distillation-small/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.*2023 · 140 citations*
681. [**Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration arXiv:2307.05300**Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al.*2023 · 140 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-unleashing-emergent-cognitive-synergy-large/)
682. **[Representing Numbers in NLP: a Survey and a Vision](https://md.research.elamur.ai/free/en/papers/thawani-2021-representing-numbers-nlp-survey-vision/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Avijit Thawani, Jay Pujara, Filip Ilievski, et al.*2021 · 139 citations*
683. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning arXiv:2409.12183**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 138 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
684. [**Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models arXiv:2212.08037**Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.*2022 · 138 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
685. **[ConvoKit: A Toolkit for the Analysis of Conversations](https://md.research.elamur.ai/free/en/papers/chang-2020-convokit-toolkit-analysis-conversations/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Jonathan P. Chang, Caleb Chiam, Liye Fu, et al.*2020 · 138 citations*
686. **[Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets](https://md.research.elamur.ai/free/en/papers/liu-2019-inoculation-fine-tuning-method-analyzing/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nelson F. Liu, Roy Schwartz, Noah A. Smith*2019 · 138 citations*
687. **[InFoBench: Evaluating Instruction Following Ability in Large Language Models](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 136 citations*
688. [**A fine-grained comparison of pragmatic language understanding in humans and language models**Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
689. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions arXiv:2309.07875**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
690. [**Secrets of RLHF in Large Language Models Part I: PPO arXiv:2307.04964**Rui Zheng, Shihan Dou, Songyang Gao, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-secrets-rlhf-large-language-models/)
691. **[Self-Knowledge Guided Retrieval Augmentation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-self-knowledge-guided-retrieval-augmentation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yile Wang, Peng Li, Maosong Sun, et al.*2023 · 135 citations*
692. **[Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models](https://md.research.elamur.ai/free/en/papers/yang-2021-be-careful-about-poisoned-word/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Wenkai Yang, Lei Li, Zhiyuan Zhang, et al.*2021 · 135 citations*
693. **[UnNatural Language Inference](https://md.research.elamur.ai/free/en/papers/sinha-2021-unnatural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, et al.*2021 · 135 citations*
694. **[Interpretable Neural Predictions with Differentiable Binary Variables](https://md.research.elamur.ai/free/en/papers/bastings-2019-interpretable-neural-predictions-differentiable-binary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jasmijn Bastings, Wilker Aziz, Ivan Titov*2019 · 135 citations*
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696. **[Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View](https://md.research.elamur.ai/free/en/papers/zhang-2024-exploring-collaboration-mechanisms-llm-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jintian Zhang, Xin Xu, Ningyu Zhang, et al.*2024 · 134 citations*
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