Domain Knowledge
Domain knowledge integration into large language models (LLMs) is a crucial area of research aiming to enhance the accuracy, reliability, and explainability of LLMs for domain-specific tasks. Current efforts focus on incorporating domain knowledge through various methods, including knowledge graphs, ontologies, and retrieval-augmented generation (RAG), often employing architectures like mixture-of-experts models and neurosymbolic agents. This research is significant because it addresses the limitations of general-purpose LLMs in specialized fields, leading to improved performance in applications ranging from medical diagnosis to scientific discovery and financial analysis.
Papers
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods
Mohammed Sabry, Anya Belz
CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning
Zheqi He, Xinya Wu, Pengfei Zhou, Richeng Xuan, Guang Liu, Xi Yang, Qiannan Zhu, Hua Huang
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
Carolin Holtermann, Markus Frohmann, Navid Rekabsaz, Anne Lauscher
Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context
Somnath Banerjee, Amruit Sahoo, Sayan Layek, Avik Dutta, Rima Hazra, Animesh Mukherjee