Papers with universal framework
Progressive Multimodal Reasoning via Active Retrieval (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to improve multimodal large language models' reasoning performance are limited. |
| Approach: | They propose a framework to progressively improve multimodal reasoning capabilities . they propose active retrieval and Monte Carlo tree search to improve MLLMs' reasoning . |
| Outcome: | The proposed framework improves multimodal reasoning capabilities in multimodal large language models. |
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information (2023.acl-long)
Copied to clipboard
| Challenge: | Several methods for characterizing datasets based on model-driven meta-information have been developed, but the relationship and complementary effects of these methods have received less attention. |
| Approach: | They propose a framework that captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. |
| Outcome: | The proposed framework outperforms baselines in three real-world applications and can be used in a variety of real-time problems. |
Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)
Copied to clipboard
Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang
| Challenge: | Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility. |
| Approach: | They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step. |
| Outcome: | The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets. |
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time (2025.emnlp-main)
Copied to clipboard
Junyu Zhang, Runpei Dong, Han Wang, Xuying Ning, Haoran Geng, Peihao Li, Xialin He, Yutong Bai, Jitendra Malik, Saurabh Gupta, Huan Zhang
| Challenge: | Existing monotonic scaling methods for large reasoning models are not reliable. |
| Approach: | They propose a universal framework for modulating reasoning progress in large reasoning models at test time. |
| Outcome: | The proposed framework unifies and generalizes existing monotonic scaling methods and enables flexible and dense slow-to-fast reasoning modulation. |
Modeling Morphological Typology for Unsupervised Learning of Language Morphology (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to morphological analysis relied on hand-built rules to identify word-internal structures. |
| Approach: | They propose a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphology. |
| Outcome: | The proposed model outperforms existing systems on nine typologically and genetically diverse languages and shows superior performance over leading systems. |
LlmFixer: Fix the Helpfulness of Defensive Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Several defense strategies have been introduced to defend against jailbreak attacks, but these strategies weakened the usefulness of large language models. |
| Approach: | They propose a framework that acts on large language models equipped with any defense strategy to recover their usefulness. |
| Outcome: | The proposed framework can be used on large language models to recover their usefulness without updating the parameters of a defensive large language model. |