9 citations · 16 across the 9 of their papers we have counts for
4 papers · 1 filter
Conceptual In-Context Learning and Chain of Concepts: Solving Complex Conceptual Problems Using Large Language Models
Nishtha N. Vaidya, Thomas Runkler, Thomas Hubauer +2
Science and engineering problems fall in the category of complex conceptual problems that require specific conceptual information (CI) like math/logic -related know-how, process in…
FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
Yongjian Tang, Rakebul Hasan, Thomas Runkler
Large Language Models (LLMs) have provided a new pathway for Named Entity Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting methods avoid the need for train…
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering
Cuong Nhat Ha, Shima Asaadi, Sanjeev Kumar Karn +3
Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to main…
Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables
Aneta Koleva, Martin Ringsquandl, Ahmed Hatem +2
Interest in solving table interpretation tasks has grown over the years, yet it still relies on existing datasets that may be overly simplified. This is potentially reducing the ef…