---
title: "Who cites Finetuned Language — page 5 — 1,887 citing papers"
description: "1,887 papers of the Elamur Research library cite «Finetuned Language Models Are Zero-Shot Learners», found in their bibliographies · page…"
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# Who cites «Finetuned Language Models Are Zero-Shot Learners»

1,887 papers of our library cite it (of the 149,439 whose bibliographies we read) · 499 with a page here, the most cited first · page 5 of 5 · 401–499 · [← back to «Finetuned Language Models Are Zero-Shot Learners»](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)

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401. [**The ICL Consistency Test arXiv:2312.04945**Lucas Weber, Elia Bruni, Dieuwke Hupkes*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/weber-2023-icl-consistency-test/)
402. **[Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs](https://md.research.elamur.ai/free/en/papers/yang-2025-confidence-v-s-critique-decomposition/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhe Yang, Yichang Zhang, Yudong Wang, et al.*2025 · 11 citations*
403. [**EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning arXiv:2410.10209**Dong Huang, Guangtao Zeng, Jianbo Dai, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-efficoder-enhancing-code-generation-large/)
404. **[Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets](https://md.research.elamur.ai/free/en/papers/azime-2024-walia-llm-enhancing-amharic-llama/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, et al.*2024 · 11 citations*
405. [**CLEVA: Chinese Language Models EVAluation Platform arXiv:2308.04813**Yanyang Li, Jianqiao Zhao, Duo Zheng, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cleva-chinese-language-models/)
406. [**Mixed Distillation Helps Smaller Language Model Better Reasoning arXiv:2312.10730**Chenglin Li, Qianglong Chen, Liangyue Li, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-mixed-distillation-helps-smaller-language/)
407. **[Factual Dialogue Summarization via Learning from Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2025-factual-dialogue-summarization-learning-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Rongxin Zhu, Jey Han Lau, Jianzhong Qi*2025 · 10 citations*
408. **[IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization](https://md.research.elamur.ai/free/en/papers/frikha-2025-incognitext-privacy-enhancing-conditional-text/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Ahmed Frikha, Nassim Walha, Krishna Kanth Nakka, et al.*2025 · 10 citations*
409. **[Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs](https://md.research.elamur.ai/free/en/papers/mahfuz-2025-too-late-train-too-early/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Tamzeed Mahfuz, Satak Kumar Dey, Ruwad Naswan, et al.*2025 · 10 citations*
410. **[AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following](https://md.research.elamur.ai/free/en/papers/he-2026-advancedif-rubric-based-benchmarking-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yun He, Wenzhe Li, Hejia Zhang, et al.*2026 · 9 citations*
411. **[Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons](https://md.research.elamur.ai/free/en/papers/leng-2025-understanding-multi-task-learning-generalization/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yongqi Leng, Deyi Xiong*2025 · 9 citations*
412. **[Nudging: Inference-time Alignment of LLMs via Guided Decoding](https://md.research.elamur.ai/free/en/papers/fei-2025-nudging-inference-time-alignment-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Fei, Yasaman Razeghi, Sameer Singh*2025 · 8 citations*
413. **[The Nature of NLP: Analyzing Contributions in NLP Papers](https://md.research.elamur.ai/free/en/papers/pramanick-2025-nature-nlp-analyzing-contributions-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aniket Pramanick, Yufang Hou, Saif M. Mohammad, et al.*2025 · 8 citations*
414. [**Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation arXiv:2401.06477**Tianyu Zheng; Shuyue Guo; Xingwei Qu; Jiawei Guo; Xinrun Du; Qi Jia; Chenghua Lin; Wenhao Huang; Jie Fu;, Ge Zhang*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2024-kun-answer-polishment-chinese-self/)
415. **[Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/) [Workshop on the Role of Resources in the Age of Large Language Models](https://md.research.elamur.ai/free/en/venues/resourceful/)**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*
416. **[Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://md.research.elamur.ai/free/en/papers/szep-2026-fine-tuning-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, et al.*2026 · 7 citations*
417. **[Can LLMs Help Create Grammar?: Automating Grammar Creation for Endangered Languages with In-Context Learning](https://md.research.elamur.ai/free/en/papers/spencer-2025-can-llms-help-create-grammar/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Piyapath T. Spencer, Nanthipat Kongborrirak*2025 · 7 citations*
418. **[Case2Code: Scalable Synthetic Data for Code Generation](https://md.research.elamur.ai/free/en/papers/shao-2025-case2code-scalable-synthetic-data-code/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yunfan Shao, Linyang Li, Yichuan Ma, et al.*2025 · 7 citations*
419. **[Language Adaptation of Large Language Models: An Empirical Study on LLaMA2](https://md.research.elamur.ai/free/en/papers/wang-2025-language-adaptation-large-language-models/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Shumin Wang, Yuexiang Xie, Bolin Ding, et al.*2025 · 7 citations*
420. **[UltraIF: Advancing Instruction Following from the Wild](https://md.research.elamur.ai/free/en/papers/an-2025-ultraif-advancing-instruction-following-wild/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kaikai An, Li Sheng, Ganqu Cui, et al.*2025 · 7 citations*
421. [**RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework**Yifan Wang, Vera Demberg*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-rsa-control-pragmatics-grounded-lightweight/)
422. **[Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs](https://md.research.elamur.ai/free/en/papers/siddique-2026-shifting-perspectives-steering-vectors-robust/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zara Siddique, Irtaza Khalid, Liam Turner, et al.*2026 · 6 citations*
423. **[Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-insight-over-sight-exploring-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyuan Liu, Wenxuan Wang, Youliang Yuan, et al.*2025 · 6 citations*
424. **[Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs](https://md.research.elamur.ai/free/en/papers/anshumann-2025-sparse-logit-sampling-accelerating-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anshumann, Mohd Abbas Zaidi, Akhil Kedia, et al.*2025 · 6 citations*
425. **[Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory](https://md.research.elamur.ai/free/en/papers/li-2025-understanding-thinking-process-reasoning-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ming Li, Nan Zhang, Chenrui Fan, et al.*2025 · 6 citations*
426. **[Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm](https://md.research.elamur.ai/free/en/papers/li-2025-add-one-incremental-sample-selection/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhuo Li, Yuhao Du, Xiaoqi Jiao, et al.*2025 · 5 citations*
427. [**Irony Detection, Reasoning and Understanding in Zero-shot Learning arXiv:2501.16884**Peiling Yi, Yuhan Xia, Yunfei Long*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/yi-2025-irony-detection-reasoning-understanding-zero/)
428. [**Baichuan Alignment Technical Report arXiv:2410.14940**Mingan Lin, Fan Yang, Yanjun Shen, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-baichuan-alignment-technical-report/)
429. **[Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering](https://md.research.elamur.ai/free/en/papers/venkateswaran-2026-spotlight-your-instructions-instruction-following/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Praveen Venkateswaran, Danish Contractor*2026 · 4 citations*
430. **[Large Language Models as a Normalizer for Transliteration and Dialectal Translation](https://md.research.elamur.ai/free/en/papers/alam-2025-large-language-models-as-normalizer/) [Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)](https://md.research.elamur.ai/free/en/venues/vardial/)**Md Mahfuz Ibn Alam, Antonios Anastasopoulos*2025 · 4 citations*
431. **[Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Models](https://md.research.elamur.ai/free/en/papers/srirag-2025-predicting-target-word-game-playing/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dipankar Srirag, Aditya Joshi, Jacob Eisenstein*2025 · 4 citations*
432. **[SDD: Self-Degraded Defense against Malicious Fine-tuning](https://md.research.elamur.ai/free/en/papers/chen-2025-sdd-self-degraded-defense-against/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**ZiXuan Chen, Weikai Lu, Xin Lin, et al.*2025 · 4 citations*
433. [**GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning arXiv:2407.02147**Hasna Chouikhi, Manel Aloui, Cyrine Ben Hammou, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/chouikhi-2024-gemmar-enhancing-llms-through-arabic/)
434. **[Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection](https://md.research.elamur.ai/free/en/papers/pastorino-2026-decoding-news-narratives-critical-analysis/) [Workshop on Natural Language Processing for Political Sciences](https://md.research.elamur.ai/free/en/venues/politicalnlp/)**Valeria Pastorino, Jasivan Alex Sivakumar, Nafise Sadat Moosavi*2026 · 3 citations*
435. **[Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/wang-2026-learning-while-staying-curious-entropy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Wang, Hao Gu, Hongming Piao, et al.*2026 · 3 citations*
436. **[Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates](https://md.research.elamur.ai/free/en/papers/yamaguchi-2026-mitigating-catastrophic-forgetting-target-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio, et al.*2026 · 3 citations*
437. **[Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate](https://md.research.elamur.ai/free/en/papers/yi-2026-latent-agents-post-training-procedure/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Seon Keun Yi, Aaron Mueller, Dokyun Lee*2026 · 2 citations*
438. **[Qomhrá: A Bilingual Irish and English Large Language Model](https://md.research.elamur.ai/free/en/papers/mcinerney-2026-qomhra-bilingual-irish-english-large/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Joseph McInerney, Khanh-Tung Tran, Liam Lonergan, et al.*2026 · 2 citations*
439. [**A Platform for Generating Educational Activities to Teach English as a Second Language arXiv:2504.20251**Aiala Rosa, Santiago Gongora, Juan Pablo Filevich, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/rosa-2025-platform-generating-educational-activities-teach/)
440. **[Compatibility-Aware Dynamic Fine-Tuning for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-compatibility-aware-dynamic-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yucheng Zhou, Junwei Sheng, Qianning Wang, et al.*2026 · 1 citation*
441. **[DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management](https://md.research.elamur.ai/free/en/papers/yin-2026-dmretriever-family-models-improved-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kai Yin, Xiangjue Dong, Chengkai Liu, et al.*2026 · 1 citation*
442. **[Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://md.research.elamur.ai/free/en/papers/yamaguchi-2026-enhancing-linguistic-competence-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras*2026 · 1 citation*
443. **[PromptLab: A Collaborative Platform for Prompt Engineering and Dataset Curation](https://md.research.elamur.ai/free/en/papers/shaibani-2026-promptlab-collaborative-platform-prompt-engineering/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Maged S. Al-shaibani, Zaid Alyafeai, Dania Refai, et al.*2026 · 1 citation*
444. **[Revealing Procedural Reasoning Structures in Chain-of-Thought Training via Span-Level Gradient Organization](https://md.research.elamur.ai/free/en/papers/liu-2026-revealing-procedural-reasoning-structures-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jia Liu, Jiaxin Luo, Weiwen Xu, et al.*2026 · 1 citation*
445. **[SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models](https://md.research.elamur.ai/free/en/papers/nghiem-2026-smarter-data-efficient-framework-improve/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huy Nghiem, Advik Sachdeva, Hal Daumé Iii*2026 · 1 citation*
446. **[UIT-Polar at SemEval-2026 Task 9 Detecting Multilingual, Multicultural and Multievent Online Polarization](https://md.research.elamur.ai/free/en/papers/tran-2026-uit-polar-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Hoàn Trần*2026 · 1 citation*
447. **[x1: Learning to Think Adaptively Across Languages and Cultures](https://md.research.elamur.ai/free/en/papers/ye-2026-x1-learning-think-adaptively-across/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yangfan Ye, Xiaocheng Feng, Xiachong Feng, et al.*2026 · 1 citation*
448. **[Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve Tasks](https://md.research.elamur.ai/free/en/papers/madabushi-2025-construction-grammar-evidence-how-llms/) [International Workshop on Construction Grammars and NLP](https://md.research.elamur.ai/free/en/venues/cxgsnlp/)**Harish Tayyar Madabushi, Claire Bonial*2025 · 1 citation*
449. **[A Layer-wise Analysis of Supervised Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhao-2026-layer-wise-analysis-supervised-fine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qinghua Zhao, Xueling Gong, Xinyu Chen, et al.*2026*
450. [**ArabicNumBench: Evaluating Arabic Number Reading in Large Language Models arXiv:2602.18776**Anas Alhumud, Abdulaziz Alhammadi, Muhammad Badruddin Khan*2026*](https://md.research.elamur.ai/free/en/papers/alhumud-2026-arabicnumbench-evaluating-arabic-number-reading/)
451. **[Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/phan-2026-beyond-reasoning-gains-mitigating-general/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hoang Phan, Xianjun Yang, Yuanshun Yao, et al.*2026*
452. **[Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World](https://md.research.elamur.ai/free/en/papers/wu-2026-beyond-static-synthetic-noise-assessing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yulong Wu, Viktor Schlegel, Riza Batista-Navarro*2026*
453. **[CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations](https://md.research.elamur.ai/free/en/papers/shi-2026-coda-restoring-contextual-dominance-copy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**JinWei Shi, Qizhuo Xie, Qianzi Hou, et al.*2026*
454. **[CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum](https://md.research.elamur.ai/free/en/papers/wen-2026-coderise-bootstrapping-llms-ultra-low/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tengfei Wen, Xuanang Chen, Ben He, et al.*2026*
455. **[Decoupled Reasoning with Implicit Fact Tokens (DRIFT): A Dual-Model Framework for Efficient Long-Context Inference](https://md.research.elamur.ai/free/en/papers/xie-2026-decoupled-reasoning-implicit-fact-tokens/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Xie, Xuhong Wang*2026*
456. **[Delayed Wh-Question Development in Children with Hearing Loss: Evidence for Morphosyntactic Vulnerability from Corpus-Based NLP and LLM Analyses](https://md.research.elamur.ai/free/en/papers/wu-2026-delayed-wh-question-development-children/) [Workshop on Linguistic Analysis for Health](https://md.research.elamur.ai/free/en/venues/healing/)**Tong Wu*2026*
457. **[DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections](https://md.research.elamur.ai/free/en/papers/shin-2026-dynamixsft-dynamic-mixture-optimization-instruction/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haebin Shin, Lei Ji, Xiao Liu, et al.*2026*
458. **[D²-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding](https://md.research.elamur.ai/free/en/papers/zhang-2026-d-2-rag-dual-decision/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinshuo Zhang, Xiaoding Zhou, Weiyu Zhang, et al.*2026*
459. **[From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding](https://md.research.elamur.ai/free/en/papers/wang-2026-recognition-reasoning-benchmarking-enhancing-mllms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yandi Wang, Libin Zhan, Ziwei Huang, et al.*2026*
460. **[GCIG: GraphRAG-based Cross-document Instruction Generation for Boosting LLM Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-gcig-graphrag-based-cross-document/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoliang Xu, Huang Yuan, Junmei Wang, et al.*2026*
461. **[How to Make LMs Strong Node Classifiers?](https://md.research.elamur.ai/free/en/papers/xu-2026-how-make-lms-strong-node/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhe Xu, Kaveh Hassani, Si Zhang, et al.*2026*
462. **[InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026*
463. **[Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization](https://md.research.elamur.ai/free/en/papers/xueyun-2026-learning-mistakes-negative-reasoning-samples/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tian Xueyun, MingHua Ma, Bingbing Xu, et al.*2026*
464. **[LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation](https://md.research.elamur.ai/free/en/papers/zhong-2026-lleot-privacy-enhancing-offsite-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jin Zhong, Jinglin Liang, Tongtong Yang, et al.*2026*
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471. **[Position: Scores Without Context? Rethinking the Role of Evaluation in the Era of LLMs](https://md.research.elamur.ai/free/en/papers/zhou-2026-position-scores-without-context-rethinking/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Jiawei Zhou*2026*
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484. [**SteuerLLM: Local specialized large language model for German tax law analysis arXiv:2602.11081 · Scientific Reports, Volume 16, 2026**Sebastian Wind, Jeta Sopa, Laurin Schmid, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wind-2026-steuerllm-local-specialized-large-language/)
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486. [**The Validity of Coreference-based Evaluations of Natural Language Understanding arXiv:2602.16200**Ian Porada*2026*](https://md.research.elamur.ai/free/en/papers/porada-2026-validity-coreference-based-evaluations-natural/)
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488. **[Ultra-Low-Dimensional Prompt Tuning via Random Projection](https://md.research.elamur.ai/free/en/papers/wu-2026-ultra-low-dimensional-prompt-tuning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zijun Wu, Yongchang Hao, Lili Mou*2026*
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492. **[What Tokens Truly Matter? The Logit Conflation Problem in LLM Sampling](https://md.research.elamur.ai/free/en/papers/zhao-2026-what-tokens-truly-matter-logit/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pinlong Zhao, Huijun Tang, Pengfei Jiao, et al.*2026*
493. [**When Discourse Pressures Conflict: Information Structure in Vision-Language Model Outputs arXiv:2605.28346**Marcell Fekete, Johannes Bjerva, Tamas Kaldi*2026*](https://md.research.elamur.ai/free/en/papers/fekete-2026-discourse-pressures-conflict-information-structure/)
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496. **[SLENDER: Structured Outputs for SLM-based NER in Low-Resource Englishes](https://md.research.elamur.ai/free/en/papers/ren-2025-slender-structured-outputs-slm-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nicole Ren, James Teo*2025*
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