We propose a diversified prompting strategy to address the challenges of slot filling with Large Language Models (LLMs), where recall often suffers from prediction omissions and precision declines due to duplicate or excessive slot assignments. Our strategy combines sub-prompt, which partitions candidate slots into smaller groups to improve recall, and multi-view prompt, which applies diverse structural prompt variations to the same utterance. Final slot predictions are selected through threshold-based majority voting, effectively balancing recall and precision. Experiments on three benchmark datasets (SNIPS, MASSIVE, and MultiWoz) with six LLMs (bloomz, falcon, llama2, llama3, qwen2, and gemma) show consistent improvements when compared with the baseline and all single-prompt methods. For example, on SNIPS, llama3-8B improves recall from 78.4 to 90.5 and F1 from 72.6 to 82.0. Additionally, we conducted experiments across various model sizes to confirm the general applicability of our methodology. These results demonstrate that the proposed diversified prompting strategy effectively restores balance among recall, precision, and F1, offering a scalable methodology for enhancing LLM-based slot filling.

In this paper, we propose a Korean abstractive text summarization approach that uses a multi -encoder transformer. Recently, in many natural language processing (NLP) tasks, the use of the pre-trained language models (PLMs) for transfer learning has achieved remarkable performance. In particular, transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) are used for pre-training and applied to downstream tasks, showing state-of-the-art performance including abstractive text summarization. However, existing text summarization models usually use one pre-trained model per model architecture, meaning that it becomes necessary to choose one PLM at a time. For PLMs applicable to Korean abstractive text summarization, there are publicly available BERT-based pre-trained Korean models that offer different advantages such as Multilingual BERT, KoBERT, HanBERT, and KorBERT. We assume that if these PLMs could be leveraged simultaneously, better performance would be obtained. We propose a model that uses multiple encoders which are capable of leveraging multiple pre-trained models to create an abstractive summary. We evaluate our method using three benchmark Korean abstractive summarization datasets, each named Law (AI-Hub), News (AI-Hub), and News (NIKL) datasets. Experimental results show that the proposed multi-encoder model variations outperform single -encoder models. We find the empirically best summarization model by determining the optimal input combination when leveraging multiple PLMs with the multi-encoder method.