Large Language Models (LLMs) excel in general tasks but struggle in specialized domains like healthcare due to limited domain-specific knowledge. Supervised Fine-Tuning (SFT) data construction for domain adaptation often relies on heuristic methods, such as GPT-4 annotation or manual data selection, with a datacentric focus on presumed diverse, high-quality datasets. However, these methods overlook the model's inherent knowledge distribution, introducing noise, redundancy, and irrelevant data, leading to a mismatch between the selected data and the model's learning task, resulting in suboptimal performance. To address this, we propose a two-stage model-centric data selection framework, Decomposed Difficulty Data Selection (3DS), which aligns data with the model's knowledge distribution for optimized adaptation. In Stage 1, we apply Prompt-Driven Data Selection via Explicit Alignment, where the model filters irrelevant or redundant data based on its internal knowledge. In Stage 2, we perform Decomposed Difficulty Data Selection, where data selection is guided by our defined difficulty decomposition, using three metrics: Instruction Understanding, Response Confidence, and Response Correctness. This two-stage approach ensures the selected data is not only aligned with the model's knowledge and preferences but also appropriately challenging for the model to learn, leading to more effective and targeted domain adaptation. In the case study of the medical domain, our extensive experiments on real-world healthcare datasets demonstrate the superiority of 3DS over existing methods in accuracy by over 5.29%. Our dataset and code will be open-sourced at https://anonymous.4open.science/r/3DS-E67F. Large Language Models (LLMs) like GPT-4 (OpenAI, 2023) have showcased significant potential in natural language understanding. Open-source models such as LLaMA (Touvron et al., 2023) and Qwen (Bai et al., 2023) have also rapidly advanced, delivering competitive performance.

Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. How well LLMs accomplish event reasoning on various relations and reasoning paradigms remains unknown. To mitigate this disparity, we comprehensively evaluate the abilities of event reasoning of LLMs. We introduce a novel benchmark EV2 for EValuation of EVent reasoning. EV2 consists of two levels of evaluation of schema and instance and is comprehensive in relations and reasoning paradigms. We conduct extensive experiments on EV2. We find that LLMs have abilities to accomplish event reasoning but their performances are far from satisfactory. We also notice the imbalance of event reasoning abilities in LLMs. Besides, LLMs have event schema knowledge, however, they're not aligned with humans on how to utilize the knowledge. Based on these findings, we introduce two methods to guide the LLMs to utilize the event schema knowledge. Both methods achieve improvements.

We explore how the rise of Large Language Models (LLMs) significantly impacts task performance in the field of Natural Language Processing. We focus on two strategies, Retrieval-Augmented Generation (RAG) and Fine-Tuning (FT), and propose the Hypothesis Knowledge Graph Enhanced (HyKGE) framework, leveraging a knowledge graph to enhance medical LLMs. By integrating LLMs and knowledge graphs, HyKGE demonstrates superior performance in addressing accuracy and interpretability challenges, presenting potential applications in the medical domain. Our evaluations using real-world datasets highlight HyKGE's superiority in providing accurate knowledge with precise confidence, particularly in complex and difficult scenarios. The code will be available until published.