Papers with functional modules
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
Copied to clipboard
Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022.emnlp-demos)
Copied to clipboard
Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li
| Challenge: | Existing knowledge extraction tools are not complete due to emerging entities and relations in real-world applications. |
| Approach: | They propose an open-source knowledge extraction toolkit DeepKE that supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
| Outcome: | The proposed toolkit supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet (2021.acl-demo)
Copied to clipboard
| Challenge: | CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. |
| Approach: | They propose an information extraction toolkit called CogIE that is a bridge connecting raw texts and CogNet. |
| Outcome: | The proposed toolkit can ground raw texts to CogNet and leverage different types of knowledge to enrich extracted results. |
Sentiment Tagging with Partial Labels using Modular Architectures (P19-1)
Copied to clipboard
| Challenge: | Many NLP learning tasks can be decomposed into sub-tasks, each associated with a partial label. |
| Approach: | They propose a modular learning approach where sub-tasks are learned using functional modules . they propose 'learning with partial labels' approach that decomposes tasks into partial labels . |
| Outcome: | The proposed approach can simplify learning and reduce supervision efforts. |
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods (2024.findings-eacl)
Copied to clipboard
| Challenge: | Transfer learning methods have shown that reusing non-task-specific knowledge can speed up task-specific learning in the latter. |
| Approach: | They propose to port whole functional modules that encode task-specific knowledge from one model to another using parameter-efficient finetuning techniques. |
| Outcome: | The proposed methods outperform the two alternatives, but there are differences between the two. |
Rethinking Task-Oriented Dialogue Systems: From Complex Modularity to Zero-Shot Autonomous Agent (2024.acl-long)
Copied to clipboard
| Challenge: | Task-oriented dialogue systems are designed to be composed of several functional modules, but lacks a general-purpose instruction-following language model. |
| Approach: | They propose a fully zero-shot autonomous TOD agent that leverages a general-purpose instruction-following language model to decide what to do at each dialogue turn. |
| Outcome: | The proposed agent can perform tasks in real-life scenarios with a general-purpose instruction-following language model. |
AGGC: Adaptive Group Gradient Clipping for Stabilizing Large Language Model Training (2026.findings-acl)
Copied to clipboard
| Challenge: | Adaptive group-wise gradient clipping (AGGC) is a new approach to stabilize training of Large Language Models. |
| Approach: | They propose a method to stabilize gradient clipping by partitioning parameters into groups based on functional types and a time-dependent scheduling mechanism to balance exploration and convergence. |
| Outcome: | The proposed algorithm outperforms standard LoRA and achieves 72.93% accuracy . it can be integrated into existing pipelines with negligible overhead. |
Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making (2025.emnlp-main)
Copied to clipboard
Yejin Son, Minseo Kim, Sungwoong Kim, Seungju Han, Jian Kim, Dongju Jang, Youngjae Yu, Chan Young Park
| Challenge: | Existing safety evaluations rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. |
| Approach: | They propose a framework for systematically evaluating the physical safety of LLMs in embodied decision making. |
| Outcome: | The proposed framework assesses the physical safety of LLMs in embodied decision making. |