Large language models (LLMs) have been widely adopted as the core of agent frameworks in various scenarios, such as social simulations and AI companions. However, the extent to which they can replicate human-like motivations remains an underexplored question. Existing benchmarks are constrained by simplistic scenarios and the absence of character identities, resulting in an information asymmetry with real-world situations. To address this gap, we propose MotiveBench, which consists of 200 rich contextual scenarios and 600 reasoning tasks covering multiple levels of motivation. Using MotiveBench, we conduct extensive experiments on seven popular model families, comparing different scales and versions within each family. The results show that even the most advanced LLMs still fall short in achieving human-like motivational reasoning. Our analysis reveals key findings, including the difficulty LLMs face in reasoning about “love & belonging” motivations and their tendency toward excessive rationality and idealism. These insights highlight a promising direction for future research on the humanization of LLMs.

Medical information retrieval (MIR) is vital for accessing knowledge from electronic health records, scientific literature, and medical databases, supporting applications such as medical education, patient queries, and clinical diagnosis. However, effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. To address this challenge, we propose **S**elf-**L**earning **Hy**pothetical **D**ocument **E**mbeddings (**SL-HyDE**), a framework that leverages large language models (LLMs) to generate hypothetical documents conditioned on a query. These documents encapsulate essential medical context, guiding dense retrievers toward the most relevant results. SL-HyDE further employs a self-learning mechanism that iteratively improves pseudo-document generation and retrieval using unlabeled corpora, eliminating the need for labeled data. In addition, we introduce the Chinese Medical Information Retrieval Benchmark (CMIRB), a comprehensive evaluation suite reflecting real-world medical scenarios, comprising five tasks and ten datasets. By benchmarking ten models on CMIRB, we provide a rigorous standard for evaluating MIR systems. Experimental results demonstrate that SL-HyDE significantly outperforms HyDE in retrieval accuracy, while exhibiting strong generalization and scalability across diverse LLM and retriever configurations. Our code and data are publicly available at: https://github.com/ll0ruc/AutoMIR.

Most large language models are fine-tuned using either expensive human-annotated data or GPT-4 generated data which cannot guarantee performance in certain domains. We argue that although the web-crawled data often has formatting errors causing semantic inaccuracies, it can still serve as a valuable source for high-quality supervised fine-tuning in specific domains without relying on advanced models like GPT-4. To this end, we create a paired training dataset automatically by aligning web-crawled data with a smaller set of high-quality data. By training a language model on this dataset, we can convert web data with irregular formats into high-quality ones. Our experiments show that training with the model-transformed data yields better results, surpassing training with only high-quality data by an average score of 9.4% in Chinese math problems. Additionally, our 7B model outperforms several open-source models larger than 32B and surpasses well-known closed-source models such as GPT-3.5, highlighting the efficacy of our approach. We have released our code at https://github.com/zhouj8553/Web_to_SFT.