| Challenge: | Recent advances in pretrained language models and large language models have demonstrated transformative capabilities across diverse domains. |
| Approach: | They propose a taxonomy for categorization based on tasks in the patent life cycle . they introduce a novel taxonomies for categorizing based upon tasks in patent life cycles . |
| Outcome: | The proposed method is based on tasks in the patent life cycle and provides a taxonomy for categorization based upon tasks in patent life cycles. |
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| Challenge: | PatentVision integrates textual and visual inputs to generate patent specifications . existing systems fail to capture the nuanced interplay between textual, visual components . |
| Approach: | They propose a multimodal framework that integrates textual and visual inputs to generate patent specifications. |
| Outcome: | The proposed framework surpasses text-only methods in patent writing, the authors show . it integrates visual data to better represent intricate design features and functional connections . |
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers (2022.lrec-1)
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| Challenge: | resurgence of multimodal datasets has attracted significant research interest, but there is no comprehensive survey for this task. |
| Approach: | They present a survey of a multimodal dataset with different modalities according to the applications. |
| Outcome: | The proposed datasets are available online and discuss the new frontier and motivate future researches. |
Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
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Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
| Challenge: | Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities. |
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PatentEval: Understanding Errors in Patent Generation (2024.naacl-long)
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| Challenge: | a patent is a legal instrument that grants inventors or entities exclusive rights over their invention for a designated period. |
| Approach: | They propose a typology specifically designed for evaluating two distinct tasks in machine-generated patent texts. |
| Outcome: | The proposed approach provides valuable insights into the capabilities and limitations of current language models in the specialized field of patent text generation. |
Three Real-World Datasets and Neural Computational Models for Classification Tasks in Patent Landscaping (2022.emnlp-main)
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| Challenge: | Patent Landscaping is one of the central tasks of intellectual property management and involves selecting and grouping patents according to user-defined technical or application-oriented criteria. |
| Approach: | They propose to use a novel model that takes into account textual information from the patents’ full texts as well as embeddings created based on the patent’s CPC labels. |
| Outcome: | The proposed model takes into account textual information from the patents’ full texts as well as embeddings created based on the patent’s CPC labels. |
Patentformer: A Novel Method to Automate the Generation of Patent Applications (2024.emnlp-industry)
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| Challenge: | Patentformer is a novel method for generating patent specification by fine-tuning the generative models with diverse sources of information, e.g., patent claims, drawing text, and brief descriptions of the drawings. |
| Approach: | They propose a method for generating patent specification by fine-tuning generative models with diverse sources of information, e.g., patent claims, drawing text, and brief descriptions of the drawings. |
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A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
Can Large Language Models Generate High-quality Patent Claims? (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have shown exceptional performance across various text generation tasks, but remain under-explored in the patent domain, which offers highly structured and precise language. |
| Approach: | They construct a dataset to investigate the performance of current LLMs in patent claim generation. |
| Outcome: | The proposed model outperforms state-of-the-art general LLMs in patent claim generation. |
MM-LLMs: Recent Advances in MultiModal Large Language Models (2024.findings-acl)
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| Challenge: | MultiModal Large Language Models (MM-LLMs) have undergone significant advances in the past year . traditional MM models incur substantial computational costs, especially when trained from scratch . |
| Approach: | They propose a taxonomy encompassing 126 MM-LLMs and summarize key training recipes to enhance their potency. |
| Outcome: | The proposed models preserve the reasoning and decision-making capabilities of LLMs and empower diverse range of MM tasks. |
A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges (2025.findings-acl)
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Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, Xuming Hu
| Challenge: | This survey provides **the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models** . integrating large language model with mathematical reasoning tasks is becoming significant as AI advances . |
| Approach: | They review over 200 studies published since 2021 and examine the state-of-the-art developments in Math-LLMs . they identify five major challenges hindering the realization of AGI in this domain . |
| Outcome: | The authors examine the state-of-the-art developments in Math-LLMs with a focus on multimodal settings. |