This paper presents our approach to the CLPsych 2024 shared task: utilizing large language models (LLMs) for finding supporting evidence about an individual’s suicide risk level in Reddit posts. Our framework is constructed around an LLM with knowledge self-generation and output refinement. The knowledge self-generation process produces task-related knowledge which is generated by the LLM and leads to accurate risk predictions. The output refinement process, later, with the selected best set of LLM-generated knowledge, refines the outputs by prompting the LLM repeatedly with different knowledge instances interchangeably. We achieved highly competitive results comparing to the top-performance participants with our official recall of 93.5%, recall–precision harmonic-mean of 92.3%, and mean consistency of 96.1%.

We describe the evaluation framework for spoken document retrieval for the IR for the Spoken Documents Task, conducted in the ninth NTCIR Workshop. The two parts of this task were a spoken term detection (STD) subtask and an ad hoc spoken document retrieval subtask (SDR). Both subtasks target search terms, passages and documents included in academic and simulated lectures of the Corpus of Spontaneous Japanese. Seven teams participated in the STD subtask and five in the SDR subtask. The results obtained through the evaluation in the workshop are discussed.