AI holds promise for transforming scientific processes, including hypothesis generation.Prior work on hypothesis generation can be broadly categorized into theory-driven and datadriven approaches.While both have proven effective in generating novel and plausible hypotheses, it remains an open question whether they can complement each other.To address this, we develop the first method that combines literature-based insights with data to perform LLM-powered hypothesis generation.We apply our method on five different datasets and demonstrate that integrating literature and data outperforms other baselines (8.97% over fewshot, 15.75% over literature-based alone, and 3.37% over data-driven alone).Additionally, we conduct the first human evaluation to assess the utility of LLM-generated hypotheses in assisting human decision-making on two challenging tasks: deception detection and AI generated content detection.Our results show that human accuracy improves significantly by 7.44% and 14.19% on these tasks, respectively.These findings suggest that integrating literature-based and data-driven approaches provides a comprehensive and nuanced framework for hypothesis generation and could open new avenues for scientific inquiry.

Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework AgentVerse that can effectively orchestrate a collaborative group of expert agents as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that AgentVerse can proficiently deploy multi-agent groups that outperform a single agent. Extensive experiments on text understanding, reasoning, coding, tool utilization, and embodied AI confirm the effectiveness of AgentVerse. Moreover, our analysis of agent interactions within AgentVerse reveals the emergence of specific collaborative behaviors, contributing to heightened group efficiency. We will release our codebase, AgentVerse, to further facilitate multi-agent research.