Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes a considerable annotation cost, and a lack of optimization methods for tailoring LLMs to specific tasks persists. To address the above issues, we propose a Planning framework for constructing Extractive-based LLMs called PlanE, which includes data decomposition, instruction tuning, and prompt inference. Additionally, we introduce a Data-Tuning-Inference (DTI) planner, aimed at selecting the optimal base-LLM and its DTI combinations for specific datasets to improve construction efficiency. The experimental results demonstrate the effectiveness of our PlanE from two views: (1) across different datasets using the same base-LLM, and (2) on the same dataset using different base-LLMs. Furthermore, we validate the generalizability of the proposed DTI planner under different optimization objectives. The codes are publicly available at https://github.com/gugugu-469/PlanE.

Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from generating inaccurate content or fabricating information without valid sources. To address these issues, we propose LCDS, a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. LCDS constructs a source mapping table by calculating the textual similarity between electronic medical records (EMRs) and discharge summaries, providing a structured reference for generation. Based on a comprehensive set of logical rules, LCDS identifies the structured writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summaries. Furthermore, LCDS traces the provenance of generated content, allowing experts to review, provide feedback, and rectify errors to produce golden discharge summaries, which are subsequently recorded for the incremental fine-tuning of LLMs.Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.

Information extraction plays a critical role in natural language processing. When applying large language models (LLMs) to this domain, we discover an unexpected phenomenon: LLMs’ spurious associations. In tasks such as relation extraction, LLMs can accurately identify entity pairs, even if the given relation (label) is semantically unrelated to the pre-defined original one. To find these labels, we design two strategies in this study, including forward label extension and backward label validation. We also leverage the extended labels to improve model performance. Our comprehensive experiments show that spurious associations occur consistently in both Chinese and English datasets across various LLM sizes. Moreover, the use of extended labels significantly enhances LLM performance in information extraction tasks. Remarkably, there is a performance increase of 9.55%, 11.42%, and 21.27% in F1 scores on the SciERC, ACE05, and DuEE datasets, respectively.