---
title: "Who cites Language Models are Few-Shot Learners — page 7"
description: "10,782 papers of the Elamur Research library cite «Language Models are Few-Shot Learners», found in their bibliographies · page 7 of 9 · 601–700; the most…"
canonical: "https://research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/page/7/"
---
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# Who cites «Language Models are Few-Shot Learners»

10,782 papers of our library cite it (of the 140,884 whose bibliographies we read) · 808 with a page here, the most cited first · page 7 of 9 · 601–700 · [← the paper](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)

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601. [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 116 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
602. [**Scaling Laws for Autoregressive Generative Modeling**Tom Henighan, Jared Kaplan, Mor Katz, et al.*2020 · 115 citations*](https://md.research.elamur.ai/free/en/papers/henighan-2020-scaling-laws-autoregressive-generative-modeling/)
603. [**Qwen3-Omni Technical Report**Jin Xu, Zhifang Guo, Hangrui Hu, et al.*2025 · 114 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen3-omni-technical-report/)
604. [**Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models**Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-inference-scaling-laws-empirical-analysis/)
605. [**GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction**Oscar Sainz, Iker Garcia-Ferrero, Rodrigo Agerri, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-gollie-annotation-guidelines-improve-zero/)
606. [**Demystifying Long Chain-of-Thought Reasoning in LLMs**Edward Yeo, Yuxuan Tong, Morry Niu, et al.*2025 · 113 citations*](https://md.research.elamur.ai/free/en/papers/yeo-2025-demystifying-long-chain-thought-reasoning/)
607. [**ALLaM: Large Language Models for Arabic and English**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
608. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/)
609. [**DistiLLM: Towards Streamlined Distillation for Large Language Models**Jongwoo Ko, Sungnyun Kim, Tianyi Chen, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/ko-2024-distillm-towards-streamlined-distillation-large/)
610. [**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/)
611. [**Benchmarking Machine Translation with Cultural Awareness**Binwei Yao, Ming Jiang, Tara Bobinac, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-benchmarking-machine-translation-cultural-awareness/)
612. [**Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification**Aojun Zhou, Ke Wang, Zimu Lu, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-solving-challenging-math-word-problems/)
613. [**RestGPT: Connecting Large Language Models with Real-World RESTful APIs**Yifan Song, Weimin Xiong, Dawei Zhu, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/song-2023-restgpt-connecting-large-language-models/)
614. [**Data Engineering for Scaling Language Models to 128K Context**Yao Fu, Rameswar Panda, Xinyao Niu, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-data-engineering-scaling-language-models/)
615. [**GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers**Qintong Li, Leyang Cui, Xueliang Zhao, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-gsm-plus-comprehensive-benchmark-evaluating/)
616. [**Evaluating the Performance of Large Language Models on GAOKAO Benchmark**Xiaotian Zhang, Chunyang Li, Yi Zong, et al.*2023 · 107 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-evaluating-performance-large-language/)
617. [**Tokenizer Choice For LLM Training: Negligible or Crucial?**Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.*2024 · 102 citations*](https://md.research.elamur.ai/free/en/papers/ali-2024-tokenizer-choice-llm-training-negligible/)
618. [**Scaling Laws for Transfer**Danny Hernandez, Jared Kaplan, Tom Henighan, et al.*2021 · 102 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2021-scaling-laws-transfer/)
619. [**Qwen2.5-1M Technical Report**An Yang, Bowen Yu, Chengyuan Li, et al.*2025 · 100 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen2-5-1m-technical/)
620. [**Rethinking Benchmark and Contamination for Language Models with Rephrased Samples**Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/)
621. [**MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning**Ke Wang, Houxing Ren, Aojun Zhou, et al.*2023 · 98 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/)
622. [**Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation**Marjan Ghazvininejad, Hila Gonen, Luke Zettlemoyer*2023 · 95 citations*](https://md.research.elamur.ai/free/en/papers/ghazvininejad-2023-dictionary-based-phrase-level-prompting/)
623. [**MAmmoTH2: Scaling Instructions from the Web**Xiang Yue, Tuney Zheng, Ge Zhang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/)
624. [**Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations**Chunkit Chan, Cheng Jiayang, Weiqi Wang, et al.*2024 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/)
625. [**InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model**Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.*2024 · 92 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-internlm-xcomposer2-mastering-free/)
626. [**WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models**Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz*2022 · 90 citations*](https://md.research.elamur.ai/free/en/papers/minixhofer-2022-wechsel-effective-initialization-subword-embeddings/)
627. [**Language Modeling Is Compression**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
628. [**The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs**Laura Ruis, Akbir Khan, Stella Biderman, et al.*2022 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ruis-2022-goldilocks-pragmatic-understanding-fine-tuning/)
629. [**Chain-of-Dictionary Prompting Elicits Translation in Large Language Models**Hongyuan Lu, Haoran Yang, Haoyang Huang, et al.*2024 · 81 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-chain-dictionary-prompting-elicits-translation/)
630. [**Are Pretrained Language Models Symbolic Reasoners over Knowledge?**Nora Kassner, Benno Krojer, Hinrich Schütze*2020 · 79 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2020-are-pretrained-language-models-symbolic/)
631. [**Coreference Resolution through a seq2seq Transition-Based System**Bernd Bohnet, Chris Alberti, Michael Collins*2023 · 77 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2023-coreference-resolution-through-seq2seq-transition/)
632. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
633. [**Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting**Chen Chen, Yufei Wang, Aixin Sun, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-dipping-plms-sauce-bridging-structure/)
634. [**Iterative Translation Refinement with Large Language Models**Pinzhen Chen, Zhicheng Guo, Barry Haddow, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-iterative-translation-refinement-large-language/)
635. [**What Artificial Neural Networks Can Tell Us About Human Language Acquisition**Alex Warstadt, Samuel R. Bowman*2022 · 72 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2022-artificial-neural-networks-tell-us/)
636. [**The SIGMORPHON 2022 Shared Task on Morpheme Segmentation**Khuyagbaatar Batsuren, Gabor Bella, Aryaman Arora*2022 · 71 citations*](https://md.research.elamur.ai/free/en/papers/batsuren-2022-morpheme-segmentation/)
637. [**CREPE: Open-Domain Question Answering with False Presuppositions**Xinyan Yu, Sewon Min, Luke Zettlemoyer, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/)
638. [**MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models**Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/kwan-2024-mt-eval-multi-turn-capabilities/)
639. [**Scaling Laws and Interpretability of Learning from Repeated Data**Danny Hernandez, Tom Brown, Tom Conerly, et al.*2022 · 62 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2022-scaling-laws-interpretability-learning-repeated/)
640. [**TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties**Karima Kadaoui, Samar M. Magdy, Abdul Waheed, et al.*2023 · 60 citations*](https://md.research.elamur.ai/free/en/papers/kadaoui-2023-tarjamat-evaluation-bard-chatgpt-machine/)
641. [**Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks**Akari Asai, Matt Gardner, Hannaneh Hajishirzi*2022 · 60 citations*](https://md.research.elamur.ai/free/en/papers/asai-2022-evidentiality-guided-generation-knowledge-intensive/)
642. [**AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level**Amit Seker, Elron Bandel, Dan Bareket, et al.*2022 · 57 citations*](https://md.research.elamur.ai/free/en/papers/seker-2022-alephbert-language-model-pre-training/)
643. [**Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs**Aaditya K. Singh, DJ Strouse*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-tokenization-counts-impact-tokenization-arithmetic/)
644. [**VariErr NLI: Separating Annotation Error from Human Label Variation**Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/genzel-2024-varierr-nli-separating-annotation-error/)
645. [**Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges**Bolei Ma, Yuting Li, Wei Zhou, et al.*2025 · 49 citations*](https://md.research.elamur.ai/free/en/papers/ma-2025-pragmatics-era-large-language-models/)
646. [**Metaphor Understanding Challenge Dataset for LLMs**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/)
647. [**DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition**Chunkit Chan, Xin Liu, Jiayang Cheng, et al.*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/)
648. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
649. [**Transcending Scaling Laws with 0.1% Extra Compute**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 40 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
650. [**Scaling Laws for Generative Mixed-Modal Language Models**Armen Aghajanyan, Lili Yu, Alexis Conneau, et al.*2023 · 39 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2023-scaling-laws-generative-mixed-modal/)
651. [**Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings**Timothee Mickus, Kees Van Deemter, Mathieu Constant, et al.*2022 · 38 citations*](https://md.research.elamur.ai/free/en/papers/mickus-2022-codwoe-dictionaries-embeddings/)
652. [**Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition**Hao Zhou, Man Lan, Yuanbin Wu, et al.*2022 · 37 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-prompt-based-connective-prediction-method/)
653. [**Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches**Daniel Fried, Nicholas Tomlin, Jennifer Hu, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/)
654. [**Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs**Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2024-towards-cross-tokenizer-distillation-universal/)
655. [**Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality**Pei Zhou, Hyundong Cho, Pegah Jandaghi, et al.*2022 · 34 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-reflect-not-reflex-inference-based/)
656. [**OpenStance: Real-world Zero-shot Stance Detection**Hanzi Xu, Slobodan Vucetic, Wenpeng Yin*2022 · 32 citations*](https://md.research.elamur.ai/free/en/papers/xu-2022-openstance-real-world-zero-shot/)
657. [**Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions**Huachuan Qiu, Zhenzhong Lan*2026 · 31 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2026-interactive-agents-simulating-counselor-client/)
658. [**TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages**Anirudh Srinivasan, Eunsol Choi*2022 · 29 citations*](https://md.research.elamur.ai/free/en/papers/srinivasan-2022-tydip-dataset-politeness-classification-nine/)
659. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
660. [**MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response**Zihao Deng, Yinghao Ma, Yudong Liu, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/deng-2024-musilingo-bridging-music-text-pre/)
661. [**Prediction of Arabic Legal Rulings using Large Language Models**Adel Ammar, Anis Koubaa, Bilel Benjdira, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2023-prediction-arabic-legal-rulings-using/)
662. [**Are Large Language Models Robust Coreference Resolvers?**Nghia T. Le, Alan Ritter*2023 · 26 citations*](https://md.research.elamur.ai/free/en/papers/le-2023-large-language-models-robust-coreference/)
663. [**IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions**Wenhao Yu, Meng Jiang, Peter Clark, et al.*2023 · 26 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/)
664. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
665. [**Assessing the Capabilities of Large Language Models in Coreference: An Evaluation**Yujian Gan, Massimo Poesio, Juntao Yu*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/gan-2024-assessing-capabilities-large-language-models/)
666. [**Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/)
667. [**Prompt Tuning with Contradictory Intentions for Sarcasm Recognition**Yiyi Liu, Ruqing Zhang, Yixing Fan, et al.*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-prompt-tuning-contradictory-intentions-sarcasm/)
668. [**Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?**Ajay Patel, Nicholas Andrews, Chris Callison-Burch*2022 · 24 citations*](https://md.research.elamur.ai/free/en/papers/patel-2022-low-resource-authorship-style-transfer/)
669. [**Distillation Scaling Laws**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
670. [**The Moral Foundations Reddit Corpus**Jackson P. Trager, Alireza S. Ziabari, Elnaz Rahmati, et al.*2026 · 22 citations*](https://md.research.elamur.ai/free/en/papers/trager-2026-moral-foundations-reddit-corpus/)
671. [**What happens before and after: Multi-Event Commonsense in Event Coreference Resolution**Sahithya Ravi, Chris Tanner, Raymond Ng, et al.*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/ravi-2023-what-happens-before-after-multi/)
672. [**CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models**Zixin Chen, Hongzhan Lin, Ziyang Luo, et al.*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-cofipara-coarse-fine-paradigm-multimodal/)
673. [**Semantics of Multiword Expressions in Transformer-Based Models: A Survey**Filip Miletić, Sabine Schulte im Walde*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/miletic-2024-semantics-multiword-expressions-transformer-based/)
674. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
675. [**Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts**Nghia T. Le, Fan Bai, Alan Ritter*2022 · 20 citations*](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/)
676. [**Can we obtain significant success in RST discourse parsing by using Large Language Models?**Aru Maekawa, Tsutomu Hirao, Hidetaka Kamigaito, et al.*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/maekawa-2024-can-we-obtain-significant-success/)
677. [**Do Large Language Models Understand Word Senses?**Domenico Meconi, Simone Stirpe, Federico Martelli*2025 · 18 citations*](https://md.research.elamur.ai/free/en/papers/meconi-2025-llms-word-senses/)
678. [**CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks**Zhaozhi Qian, Faroq Altam, Muhammad Alqurishi, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/qian-2024-cameleval-advancing-culturally-aligned-arabic/)
679. [**Elaborative Simplification as Implicit Questions Under Discussion**Yating Wu, William Sheffield, Kyle Mahowald, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-elaborative-simplification-as-implicit-questions/)
680. [**ArabianGPT: Native Arabic GPT-based Large Language Model**Anis Koubaa, Adel Ammar, Lahouari Ghouti, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/koubaa-2024-arabiangpt-native-arabic-gpt-based/)
681. [**MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Models**Dojun Park, Jiwoo Lee, Seohyun Park, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-multiprageval-multilingual-pragmatic-evaluation-large/)
682. [**New Trends for Modern Machine Translation with Large Reasoning Models**Sinuo Liu, Chenyang Lyu, Minghao Wu, et al.*2026 · 16 citations*](https://md.research.elamur.ai/free/en/papers/liu-2026-new-trends-modern-machine-translation/)
683. [**Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models' Understanding of Discourse Relations**Yisong Miao, Hongfu Liu, Wenqiang Lei, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/miao-2024-discursive-socratic-questioning-evaluating-faithfulness/)
684. [**Is Child-Directed Speech Effective Training Data for Language Models?**Steven Y. Feng, Noah D. Goodman, Michael C. Frank*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/feng-2024-is-child-directed-speech-effective/)
685. [**Rethinking Pragmatics in Large Language Models: Towards Open-Ended Evaluation and Preference Tuning**Shengguang Wu, Shusheng Yang, Zhenglun Chen, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-rethinking-pragmatics-large-language-models/)
686. [**A Large and Diverse Arabic Corpus for Language Modeling**Abbas Raza Ali, Muhammad Ajmal Siddiqui, Rema Algunaibet, et al.*2022 · 16 citations*](https://md.research.elamur.ai/free/en/papers/ali-2022-large-diverse-arabic-corpus-language/)
687. [**Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities**Shaltiel Shmidman, Avi Shmidman, Amir DN Cohen, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2024-adapting-llms-hebrew-unveiling-dictalm/)
688. [**X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection**Yaqian Han, Yekun Chai, Shuohuan Wang, et al.*2022 · 15 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-x-pudu-semeval-2022-task/)
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693. [**Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution**Ryuto Konno, Shun Kiyono, Yuichiroh Matsubayashi, et al.*2021 · 14 citations*](https://md.research.elamur.ai/free/en/papers/konno-2021-pseudo-zero-pronoun-resolution-improves/)
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