ControlRetriever: Harnessing the Power of Instructions for Controllable Retrieval

Recent studies have shown that dense retrieval models, lacking dedicated training data, struggle to perform well across diverse retrieval tasks, as different retrieval tasks often entail distinct search intents. To address this challenge, in this work we introduce ControlRetriever, a generic and efficient approach with a parameter isolated architecture, capable of controlling dense retrieval models to directly perform varied retrieval tasks, harnessing the power of instructions that explicitly describe retrieval intents in natural language. Leveraging the foundation of ControlNet, which has proven powerful in text-to-image generation, ControlRetriever imbues different retrieval models with the new capacity of controllable retrieval, all while being guided by task-specific instructions. Furthermore, we propose a novel LLM guided Instruction Synthesizing and Iterative Training strategy, which iteratively tunes ControlRetriever based on extensive automatically-generated retrieval data with diverse instructions by capitalizing the advancement of large language models. Extensive experiments show that in the BEIR benchmark, with only natural language descriptions of specific retrieval intent for each task, ControlRetriever, as a unified multi-task retrieval system without task-specific tuning, significantly outperforms baseline methods designed with task-specific retrievers and also achieves state-of-the-art zero-shot performance

Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

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