---
title: "Who cites Tenghao Huang — 415 papers of our library"
description: "415 papers of the Elamur Research library cite the works of Tenghao Huang, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/tenghao-huang/cited-by/"
---
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# Who cites Tenghao Huang

415 citations of their works from 415 papers of our library (of the 149,439 whose bibliographies we read) · 82 with a page here, the most cited first · [← Tenghao Huang](https://md.research.elamur.ai/free/en/authors/tenghao-huang/)

1. **[QLoRA: Efficient Finetuning of Quantized LLMs](https://md.research.elamur.ai/free/en/papers/dettmers-2023-qlora-efficient-finetuning-quantized-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.*2023 · 2,929 citations*
2. [**Crosslingual Generalization through Multitask Finetuning arXiv:2211.01786**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 703 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
3. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning arXiv:2301.13688**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 607 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
4. **[Large language models are few-shot clinical information extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.*2022 · 454 citations*
5. **[Large Language Models are Few-Shot Clinical Information Extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-few-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.*2022 · 384 citations*
6. **[A Survey on Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) [ACM Transactions on Software Engineering and Methodology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-software-engineering-and-methodology/)**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations*
7. [**Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! arXiv:2310.03693**Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.*2023 · 355 citations*](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/)
8. **[TIES-Merging: Resolving Interference When Merging Models](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Prateek Yadav, Derek Tam, Leshem Choshen, et al.*2023 · 324 citations*
9. [**Generate rather than Retrieve: Large Language Models are Strong Context Generators arXiv:2209.10063**Wenhao Yu, Dan Iter, Shuohang Wang, et al.*2022 · 281 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/)
10. [**LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models arXiv:2309.12307**Yukang Chen, Shengju Qian, Haotian Tang, et al.*2023 · 268 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-longlora-efficient-fine-tuning-long/)
11. **[In-context Examples Selection for Machine Translation](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sweta Agrawal, Chunting Zhou, Mike Lewis, et al.*2023 · 243 citations*
12. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
13. [**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/)
14. **[Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://md.research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, et al.*2023 · 148 citations*
15. **[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*
16. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 142 citations*
17. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
18. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
19. **[The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning](https://md.research.elamur.ai/free/en/papers/kim-2023-cot-collection-improving-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seungone Kim, Se Joo, Doyoung Kim, et al.*2023 · 118 citations*
20. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/)
21. [**LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition arXiv:2307.13269**Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lorahub-efficient-cross-task-generalization/)
22. **[BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting](https://md.research.elamur.ai/free/en/papers/yong-2023-bloom-1-adding-language-support/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, et al.*2023 · 102 citations*
23. [**Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security arXiv:2401.05459**Yuanchun Li, Hao Wen, Weijun Wang, et al.*2024 · 100 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-personal-llm-agents-insights-survey/)
24. [**LoRA+: Efficient Low Rank Adaptation of Large Models arXiv:2402.12354**Soufiane Hayou, Nikhil Ghosh, Bin Yu*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/hayou-2024-lora-efficient-low-rank-adaptation/)
25. **[LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://md.research.elamur.ai/free/en/papers/dou-2024-loramoe-alleviating-world-knowledge-forgetting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shihan Dou, Enyu Zhou, Yan Liu, et al.*2024 · 93 citations*
26. **[Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning](https://md.research.elamur.ai/free/en/papers/tan-2024-democratizing-large-language-models-personalized/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, et al.*2024 · 86 citations*
27. **[In-Context Learning with Long-Context Models: An In-Depth Exploration](https://md.research.elamur.ai/free/en/papers/bertsch-2025-context-learning-long-context-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Amanda Bertsch, Maor Ivgi, Emily Xiao, et al.*2025 · 81 citations*
28. [**S-LoRA: Serving Thousands of Concurrent LoRA Adapters MLSys 2024**Ying Sheng, Shiyi Cao, Dacheng Li, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-s-lora-serving-thousands-concurrent/)
29. [**Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities arXiv:2408.07666**Enneng Yang, Li Shen, Guibing Guo, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-model-merging-llms-mllms-methods/)
30. [**Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning arXiv:2309.05444**Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/zadouri-2023-pushing-mixture-experts-limit-extremely/)
31. [**ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models arXiv:2305.18323**Binfeng Xu, Zhiyuan Peng, Bowen Lei, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-rewoo-decoupling-reasoning-observations-efficient/)
32. [**When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks arXiv:2311.08993**Hao Peng, Xiaozhi Wang, Jianhui Chen, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-context-learning-fall-short-why/)
33. **[Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts](https://md.research.elamur.ai/free/en/papers/tan-2024-personalized-pieces-efficient-personalized-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhaoxuan Tan, Zheyuan Liu, Meng Jiang*2024 · 40 citations*
34. **[Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance](https://md.research.elamur.ai/free/en/papers/pecher-2025-comparing-specialised-small-general-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Branislav Pecher, Ivan Srba, Maria Bielikova*2025 · 24 citations*
35. **[LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering](https://md.research.elamur.ai/free/en/papers/bi-2025-llava-steering-visual-instruction-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinhe Bi, Yujun Wang, Haokun Chen, et al.*2025 · 24 citations*
36. [**InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning arXiv:2305.13627**Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu, et al.*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/cahyawijaya-2023-instructalign-high-low-resource-language/)
37. [**Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models arXiv:2401.00788**Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-astraios-parameter-efficient-instruction-tuning/)
38. [**MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning arXiv:2402.11260**Shu Yang; Muhammad Asif Ali; Cheng-Long Wang; Lijie Hu;, Di Wang*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-moral-moe-augmented-lora-llms/)
39. [**Efficiently Adapting Pretrained Language Models To New Languages arXiv:2311.05741**Zoltan Csaki, Pian Pawakapan, Urmish Thakker, et al.*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/csaki-2023-efficiently-adapting-pretrained-language-models/)
40. **[CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation](https://md.research.elamur.ai/free/en/papers/ziegler-2025-craft-your-dataset-task-specific/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ingo Ziegler, Abdullatif Köksal, Desmond Elliott, et al.*2025 · 20 citations*
41. **[Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nghia T. Le, Fan Bai, Alan Ritter*2022 · 20 citations*
42. **[Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?](https://md.research.elamur.ai/free/en/papers/razumovskaia-2025-analyzing-adapting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen*2025 · 15 citations*
43. **[L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models](https://md.research.elamur.ai/free/en/papers/jeon-2025-l4q-parameter-efficient-quantization-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hyesung Jeon, Yulhwa Kim, Jae-Joon Kim*2025 · 15 citations*
44. **[Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model](https://md.research.elamur.ai/free/en/papers/xu-2025-let-s-focus-neuron-neuron/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Haoyun Xu, Runzhe Zhan, Yingpeng Ma, et al.*2025 · 10 citations*
45. [**Investigating Forgetting in Pre-Trained Representations Through Continual Learning arXiv:2305.05968**Yun Luo, Zhen Yang, Xuefeng Bai, et al.*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-investigating-forgetting-pre-trained-representations/)
46. **[Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations](https://md.research.elamur.ai/free/en/papers/ma-2026-stable-explainable-personality-trait-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoxu Ma, Xiangbo Zhang, Zhenyu Weng*2026 · 8 citations*
47. **[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*
48. **[ArabicSense: A Benchmark for Evaluating Commonsense Reasoning in Arabic with Large Language Models](https://md.research.elamur.ai/free/en/papers/lamsiyah-2025-arabicsense-benchmark-evaluating-commonsense-reasoning/) [Workshop on Arabic Corpus Linguistics](https://md.research.elamur.ai/free/en/venues/wacl/)**Salima Lamsiyah, Kamyar Zeinalipour, Samir El amrany, et al.*2025 · 7 citations*
49. **[Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages](https://md.research.elamur.ai/free/en/papers/niyogi-2026-paramanu-compact-competitive-monolingual-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya*2026 · 5 citations*
50. **[RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates](https://md.research.elamur.ai/free/en/papers/kowsher-2025-rocoft-efficient-finetuning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, et al.*2025 · 5 citations*
51. **[Raphael at ArAIEval Shared Task: Understanding Persuasive Language and Tone, an LLM Approach](https://md.research.elamur.ai/free/en/papers/shukla-2023-raphael-araieval-shared-task-understanding/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Utsav Shukla, Manan Vyas, Shailendra Tiwari*2023 · 5 citations*
52. **[MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP](https://md.research.elamur.ai/free/en/papers/micallef-2025-melabenchv1-benchmarking-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kurt Micallef, Claudia Borg*2025 · 4 citations*
53. [**Training LLMs to Recognize Hedges in Dialogues about Roadrunner Cartoons**Amie Paige, Adil Soubki, John Murzaku, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/paige-2024-training-llms-recognize-hedges-dialogues/)
54. **[Rethinking Creativity Evaluation: A Critical Analysis of Existing Creativity Evaluations](https://md.research.elamur.ai/free/en/papers/lu-2026-rethinking-creativity-evaluation-critical-analysis/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Li-Chun Lu, Miri Liu, Pin Chun Lu, et al.*2026 · 3 citations*
55. **[rematchka at ArAIEval Shared Task: Prefix-Tuning & Prompt-tuning for Improved Detection of Propaganda and Disinformation in Arabic Social Media Content](https://md.research.elamur.ai/free/en/papers/salam-2023-rematchka-araieval-shared-task-prefix/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Reem Abdel-Salam*2023 · 3 citations*
56. **[Instant Personalized Large Language Model Adaptation via Hypernetwork](https://md.research.elamur.ai/free/en/papers/tan-2026-instant-personalized-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, et al.*2026 · 2 citations*
57. **[From Weights to Activations: Is Steering the Next Frontier of Adaptation?](https://md.research.elamur.ai/free/en/papers/ostermann-2026-weights-activations-is-steering-next/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Simon Ostermann, Daniil Gurgurov, Tanja Baeumel, et al.*2026 · 1 citation*
58. **[LitVISTA: A Benchmark for Narrative Orchestration in Literary Text](https://md.research.elamur.ai/free/en/papers/lu-2026-litvista-benchmark-narrative-orchestration-literary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingzhe Lu, Yiwen Wang, Yanbing Liu, et al.*2026 · 1 citation*
59. **[Text-to-Text Automatic Story Generation: A Survey](https://md.research.elamur.ai/free/en/papers/ma-2026-text-text-automatic-story-generation/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuan Ma, Richard Susilo, Patrik Haslum, et al.*2026 · 1 citation*
60. [**Learning from Convenience Samples: A Case Study on Fine-Tuning LLMs for Survey Non-response in the German Longitudinal Election Study arXiv:2509.25063**Tobias Holtdirk, Dennis Assenmacher, Arnim Bleier, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/holtdirk-2025-learning-convenience-samples-case-study/)
61. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
62. **[Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-Tuning](https://md.research.elamur.ai/free/en/papers/steele-2026-annotation-entropy-predicts-per-example/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Brady Steele*2026*
63. **[Automatic Annotation of Mental Health Recovery Narratives: A Benchmark Study](https://md.research.elamur.ai/free/en/papers/pandey-2026-automatic-annotation-mental-health-recovery/) [Workshop on Computational Linguistics and Clinical Psychology](https://md.research.elamur.ai/free/en/venues/clpsych/)**Shrankhla Pandey, Graham Murray, Ben Laws, et al.*2026*
64. **[Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-task-level-context-class/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianxin Zhang, Yilu Hu, Teng Liu, et al.*2026*
65. **[Context-Conditioned Masked LoRA: Dynamic Rank Routing for Compute-Efficient Parameter-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/rafiuddin-2026-context-conditioned-masked-lora-dynamic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rifat Rafiuddin, Rafae Abdullah*2026*
66. **[IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation](https://md.research.elamur.ai/free/en/papers/saxena-2026-imrnns-efficient-method-interpretable-dense/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yash Saxena, Ankur Padia, Kalpa Gunaratna, et al.*2026*
67. **[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*
68. **[Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning](https://md.research.elamur.ai/free/en/papers/liu-2026-look-within-or-beyond-theoretical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**YongKang Liu, Xingle Xu, Ercong Nie, et al.*2026*
69. **[MaskLoRA: Low-Rank Subspace–Induced Token Masking for Efficient and Faithful Language Models](https://md.research.elamur.ai/free/en/papers/rafiuddin-2026-masklora-low-rank-subspace-induced/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rifat Rafiuddin*2026*
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75. **[Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/liu-2026-specialization-without-sparsity-efficient-expressive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ganghao Liu, Qin Zhou, Zhe Wang, et al.*2026*
76. **[TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models](https://md.research.elamur.ai/free/en/papers/mu-2026-talklora-communication-aware-mixture-low/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lin Mu, Haiyang Wang, Li Ni, et al.*2026*
77. **[TARE: Lightweight Token-Aware Representation Editing for Fine-tuning Transformer-like Models](https://md.research.elamur.ai/free/en/papers/wang-2026-tare-lightweight-token-aware-representation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yulong Wang, Siyu Zhao*2026*
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80. **[Zero Shot Phonics: Evaluating Constraint-Adherent Phonics Story Generation in Large Language Models](https://md.research.elamur.ai/free/en/papers/manlises-2026-zero-shot-phonics-evaluating-constraint/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Maria Monica Manlises, Ethel Ong*2026*
81. **[🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination](https://md.research.elamur.ai/free/en/papers/mizrahi-2026-cooking-up-creativity-enhancing-llm/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Moran Mizrahi, Chen Shani, Gabriel Stanovsky, et al.*2026*
82. [**Training LLMs to Recognize Hedges in Spontaneous Narratives SIGDIAL 2024**Amie J. Paige, Adil Soubki, John Murzaku, et al.*2024*](https://md.research.elamur.ai/free/en/papers/paige-2024-training-llms-recognize-hedges-spontaneous/)

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