---
title: "Who cites Zhen Li — 490 papers of our library — Elamur Research"
description: "490 papers of the Elamur Research library cite the works of Zhen Li, found in their bibliographies; the most cited first."
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---
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# Who cites Zhen Li

490 citations of their works from 490 papers of our library (of the 149,439 whose bibliographies we read) · 72 with a page here, the most cited first · [← Zhen Li](https://md.research.elamur.ai/free/en/authors/zhen-li/)

1. [**Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding arXiv:2205.11487**Chitwan Saharia, William Chan, Saurabh Saxena, et al.*2022 · 2,533 citations*](https://md.research.elamur.ai/free/en/papers/saharia-2022-photorealistic-text-image-diffusion-models/)
2. [**Flamingo: a Visual Language Model for Few-Shot Learning arXiv:2204.14198**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,246 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
3. [**BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models arXiv:2301.12597**Junnan Li, Dongxu Li, Silvio Savarese, et al.*2023 · 2,219 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-blip-2-bootstrapping-language-image/)
4. **[CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://md.research.elamur.ai/free/en/papers/hessel-2021-clipscore-reference-free-evaluation-metric/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jack Hessel, Ari Holtzman, Maxwell Forbes, et al.*2021 · 1,550 citations*
5. [**BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation arXiv:2201.12086**Junnan Li, Dongxu Li, Caiming Xiong, et al.*2022 · 1,523 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/)
6. [**LAION-5B: An open large-scale dataset for training next generation image-text models arXiv:2210.08402**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,485 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)
7. **[Vision-Language Models for Vision Tasks: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-vision-language-models-vision-tasks/) [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-pattern-analysis-and-machine-intelligence/)**Jingyi Zhang, Jiaxing Huang, Sheng Jin, et al.*2023 · 1,047 citations*
8. **[MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Xiang Yue, Yuansheng Ni, Kai Zhang, et al.*2023 · 1,005 citations*
9. [**Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond arXiv:2308.12966**Jinze Bai, Shuai Bai, Shusheng Yang, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/)
10. [**Segment Anything arXiv:2304.02643**Alexander Kirillov, Eric Mintun, Nikhila Ravi, et al.*2023 · 960 citations*](https://md.research.elamur.ai/free/en/papers/kirillov-2023-segment-anything/)
11. **[ImageBind: One Embedding Space To Bind Them All](https://md.research.elamur.ai/free/en/papers/girdhar-2023-imagebind-one-embedding-space-bind/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, et al.*2023 · 954 citations*
12. **[Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 769 citations*
13. [**CoCa: Contrastive Captioners are Image-Text Foundation Models arXiv:2205.01917**Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al.*2022 · 667 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/)
14. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training arXiv:2212.03533**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 598 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
15. [**LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs arXiv:2111.02114**Christoph Schuhmann, Richard Vencu, Romain Beaumont, et al.*2021 · 563 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2021-laion-400m-open-dataset-clip/)
16. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
17. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 507 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
18. [**Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model arXiv:2401.09417**Lianghui Zhu, Bencheng Liao, Qian Zhang, et al.*2024 · 500 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-vision-mamba-efficient-visual-representation/)
19. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision arXiv:2108.10904**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 481 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
20. **[ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://md.research.elamur.ai/free/en/papers/chen-2023-sharegpt4v-improving-large-multi-modal/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Lin Chen, Jinsong Li, Xiaoyi Dong, et al.*2023 · 454 citations*
21. [**InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks arXiv:2312.14238**Zhe Chen, Jiannan Wu, Wenhai Wang, et al.*2023 · 435 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
22. [**Editing Models with Task Arithmetic arXiv:2212.04089**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
23. [**PaLI: A Jointly-Scaled Multilingual Language-Image Model arXiv:2209.06794**Xi Chen, Xiao Wang, Soravit Changpinyo, et al.*2022 · 371 citations*](https://md.research.elamur.ai/free/en/papers/chen-2022-pali-jointly-scaled-multilingual-language/)
24. [**LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention arXiv:2303.16199**Renrui Zhang, Jiaming Han, Chris Liu, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llama-adapter-efficient-fine-tuning/)
25. [**Text and Code Embeddings by Contrastive Pre-Training arXiv:2201.10005**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/)
26. **[MM-LLMs: Recent Advances in MultiModal Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-mm-llms-recent-advances-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Duzhen Zhang, Yahan Yu, Jiahua Dong, et al.*2024 · 320 citations*
27. **[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://md.research.elamur.ai/free/en/papers/srinivasan-2021-wit-wikipedia-based-image-text/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Krishna Srinivasan, Karthik Raman, Jiecao Chen, et al.*2021 · 315 citations*
28. **[OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/liu-2023-ocrbench-hidden-mystery-ocr-large/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Yuliang Liu, Zhang Li, Mingxin Huang, et al.*2023 · 306 citations*
29. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language arXiv:2204.00598**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 302 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
30. **[Are We on the Right Way for Evaluating Large Vision-Language Models?](https://md.research.elamur.ai/free/en/papers/chen-2024-right-way-evaluating-large-vision/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lin Chen, Jinsong Li, Xiaoyi Dong, et al.*2024 · 277 citations*
31. **[LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-llama-vid-image-worth-2/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yanwei Li, Chengyao Wang, Jiaya Jia*2023 · 262 citations*
32. [**Multimodal Few-Shot Learning with Frozen Language Models arXiv:2106.13884**Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, et al.*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/tsimpoukelli-2021-multimodal-few-shot-learning-frozen/)
33. [**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/)
34. **[DataComp: In search of the next generation of multimodal datasets](https://md.research.elamur.ai/free/en/papers/gadre-2023-datacomp-search-next-generation-multimodal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al.*2023 · 224 citations*
35. **[Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://md.research.elamur.ai/free/en/papers/deitke-2024-molmo-pixmo-open-weights-open/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Matt Deitke, Christopher Clark, Sangho Lee, et al.*2024 · 216 citations*
36. **[OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-obelics-open-web-scale-filtered/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al.*2023 · 197 citations*
37. [**InternVideo: General Video Foundation Models via Generative and Discriminative Learning arXiv:2212.03191**Yi Wang, Kunchang Li, Yizhuo Li, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-internvideo-general-video-foundation/)
38. [**PaliGemma: A versatile 3B VLM for transfer arXiv:2407.07726**Lucas Beyer, Andreas Steiner, Andre Susano Pinto, et al.*2024 · 142 citations*](https://md.research.elamur.ai/free/en/papers/beyer-2024-paligemma-versatile-3b-vlm-transfer/)
39. [**LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks arXiv:2406.18403**Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, et al.*2024 · 141 citations*](https://md.research.elamur.ai/free/en/papers/bavaresco-2024-llms-instead-human-judges-large/)
40. **[UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning](https://md.research.elamur.ai/free/en/papers/masry-2023-unichart-universal-vision-language-pretrained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, et al.*2023 · 121 citations*
41. [**Hallucination of Multimodal Large Language Models: A Survey arXiv:2404.18930**Zechen Bai, Pichao Wang, Tianjun Xiao, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-hallucination-multimodal-large-language-models/)
42. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/)
43. [**InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model arXiv:2401.16420**Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-internlm-xcomposer2-mastering-free/)
44. [**InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition arXiv:2309.15112**Pan Zhang, Xiaoyi Dong, Bin Wang, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-internlm-xcomposer-vision-language/)
45. **[SemEval-2025 Task 1: AdMIRe - Advancing Multimodal Idiomaticity Representation](https://md.research.elamur.ai/free/en/papers/pickard-2025-semeval-2025-task-1-admire/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Thomas Pickard, Aline Villavicencio, Maggie Mi, et al.*2025 · 30 citations*
46. **[MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval](https://md.research.elamur.ai/free/en/papers/zhou-2025-megapairs-massive-data-synthesis-universal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junjie Zhou, Yongping Xiong, Zheng Liu, et al.*2025 · 26 citations*
47. **[OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking](https://md.research.elamur.ai/free/en/papers/xi-2025-omnithink-expanding-knowledge-boundaries-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zekun Xi, Wenbiao Yin, Jizhan Fang, et al.*2025 · 14 citations*
48. [**MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model arXiv:2409.13729**Zhen Yang, Jinhao Chen, Zhengxiao Du, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-mathglm-vision-solving-mathematical-problems/)
49. **[Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy](https://md.research.elamur.ai/free/en/papers/na-2026-multi-session-client-centered-treatment/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hongbin Na, Tao Shen, Shumao Yu, et al.*2026 · 4 citations*
50. **[MMCLIP: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training](https://md.research.elamur.ai/free/en/papers/wu-2026-mmclip-cross-modal-attention-masked/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Biao Wu, Yutong Xie, Zeyu Zhang, et al.*2026 · 3 citations*
51. **[EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation](https://md.research.elamur.ai/free/en/papers/wang-2026-evolvr-self-evolving-pairwise-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinda Wang, Zhengxu Hou, Yangshijie Zhang, et al.*2026 · 1 citation*
52. **[microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification](https://md.research.elamur.ai/free/en/papers/silva-2026-microclip-unsupervised-clip-adaptation-coarse/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sathira Silva, Eman Ali, Chetan Arora, et al.*2026 · 1 citation*
53. **[Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning](https://md.research.elamur.ai/free/en/papers/vaishnav-2026-symbolic-grounding-reveals-representational-bottlenecks/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Mohit Vaishnav, Tanel Tammet*2026 · 1 citation*
54. **[SynthLLM: An LLM-based Scalable Synthetic Data Generation Pipeline for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/panahi-2026-synthllm-llm-based-scalable-synthetic/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Solmaz Panahi, Vasudevan Nedumpozhimana, John Kelleher*2026 · 1 citation*
55. **[Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/luo-2026-utility-oriented-visual-evidence-selection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiqing Luo, Zongye Hu, Xiao Wang, et al.*2026 · 1 citation*
56. **[When LLMs Annotate: Reliability Challenges in Low-Resource NLI](https://md.research.elamur.ai/free/en/papers/panahi-2026-when-llms-annotate-reliability-challenges/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Solmaz Panahi, John Kelleher, Vasudevan Nedumpozhimana*2026 · 1 citation*
57. **[A Large-Scale Instruction-Tuning Dataset and Models for Slovenian Vision-Language Tasks](https://md.research.elamur.ai/free/en/papers/martinc-2026-large-scale-instruction-tuning-dataset/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Matej Martinc, Domen Vreš*2026*
58. **[BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs](https://md.research.elamur.ai/free/en/papers/singha-2026-biovlm-routing-prompts-not-parameters/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mainak Singha, Tanisha Gupta, Ankit Jha, et al.*2026*
59. **[ChangJuan: A Comprehensive Benchmark for Book-Length Chinese Story Evaluation](https://md.research.elamur.ai/free/en/papers/yang-2026-changjuan-comprehensive-benchmark-book-length/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dingyi Yang, Mingshuo Wang, Qin Jin*2026*
60. **[DANCE: Diversity-attended Dynamic Caching with Asymmetric Quantization for Test-time Adaptation of Vision-Language Models](https://md.research.elamur.ai/free/en/papers/zou-2026-dance-diversity-attended-dynamic-caching/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shunge Zou, Changhu Wang, Wei Ju, et al.*2026*
61. **[DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI](https://md.research.elamur.ai/free/en/papers/zhang-2026-defgen-bench-benchmark-chinese-criminal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Senbo Zhang, Qiqi Wang, Fanghao Lou, et al.*2026*
62. **[From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation](https://md.research.elamur.ai/free/en/papers/wang-2026-words-pixels-comprehensive-survey-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yizhou Wang, Mang Tik Chiu, Lingzhi Zhang, et al.*2026*
63. **[FTibSuite: A Comprehensive Resource Suite for Tibetan Vision–Language Modeling](https://md.research.elamur.ai/free/en/papers/xu-2026-ftibsuite-comprehensive-resource-suite-tibetan/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guixian Xu, Yide Liang, Zeli Su, et al.*2026*
64. **[GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning](https://md.research.elamur.ai/free/en/papers/wang-2026-gala-hypergraph-guided-visual-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kun Wang, Li Yiming, Mingcheng Qu, et al.*2026*
65. **[LegalChainReasoner: Grounding Criminal Judicial Opinion Generation via Structured Legal Chains](https://md.research.elamur.ai/free/en/papers/shi-2026-legalchainreasoner-grounding-criminal-judicial-opinion/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weizhe Shi, Qiqi Wang, Yihong Pan, et al.*2026*
66. **[Markovian Linguistic-Temporal Bridge: Unlocking the Potential of LLMs for Time Series Forecasting](https://md.research.elamur.ai/free/en/papers/sun-2026-markovian-linguistic-temporal-bridge-unlocking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siming Sun, Kai Zhang, Xuejun Jiang, et al.*2026*
67. **[Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text](https://md.research.elamur.ai/free/en/papers/pasi-2026-multilingual-multimodal-m2m-unlocking-new/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Piyush Singh Pasi*2026*
68. **[Reward Modeling for Scientific Writing Evaluation](https://md.research.elamur.ai/free/en/papers/sahinuc-2026-reward-modeling-scientific-writing-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Furkan Şahinuç, Subhabrata Dutta, Iryna Gurevych*2026*
69. **[Semantic Contrastive Adaptation for Multimodal Figurative Language Understanding](https://md.research.elamur.ai/free/en/papers/siddiqui-2026-semantic-contrastive-adaptation-multimodal-figurative/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ayaan Siddiqui*2026*
70. **[Text-Guided Multi-Scale Frequency Representation Adaptation](https://md.research.elamur.ai/free/en/papers/yan-2026-text-guided-multi-scale-frequency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weicai Yan, Xinhua Ma, Wang Lin, et al.*2026*
71. **[Towards Unified Multimodal Large Language Models: A survey](https://md.research.elamur.ai/free/en/papers/ma-2026-unified-multimodal-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Ma, Yitian Zhang, Yun Fu*2026*
72. **[Survey on Vision-language Pre-training (视觉语言预训练综述)](https://md.research.elamur.ai/free/en/papers/yin-2022-vision-language-pre/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Jiong Yin (殷炯), Zhedong Zhang (张哲东), Yuhan Gao (高宇涵), et al.*2022*

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