Large Language Models for Information Retrieval: A Survey

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, thereby reshaping the IR landscape, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs), typified by ChatGPT and GPT-4, has revolutionized natural language processing due to their remarkable language understanding, generation, generalization, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers. Additionally, we explore promising directions within this expanding field

Genome-wide association study in Han Chinese identifies four new susceptibility loci for coronary artery disease

We performed a meta-analysis of 2 genome-wide association studies of coronary artery disease comprising 1,515 cases with coronary artery disease and 5,019 controls, followed by de novo replication studies in 15,460 cases and 11,472 controls, all of Chinese Han descent. We successfully identified four new loci for coronary artery disease reaching genome-wide significance (P < 5 × 10(−8)), which mapped in or near TTC32-WDR35, GUCY1A3, C6orf10-BTNL2 and ATP2B1. We also replicated four loci previously identified in European populations (PHACTR1, TCF21, CDKN2A/B and C12orf51). These findings provide new insights into biological pathways for the susceptibility of coronary artery disease in Chinese Han population