O pen R esearcher: Unleashing AI for Accelerated Scientific Research (2024)

Abstract: The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. Moreover, we develop various tools for OpenResearcher to understand researchers’ queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers. OpenResearcher can flexibly use these tools to balance efficiency and effectiveness. As a result, OpenResearcher enables researchers to save time and increase their potential to discover new insights and drive scientific breakthroughs. Demo, video, and code are available at: https://github.com/GAIR-NLP/OpenResearcher.

## introduction global scientific publications are growing annually by about 4%-5% @xcite , leading researchers to invest significant time and effort in thoroughly reviewing countless academic papers to find the knowledge that propels their research. this involves daily engagement with a wide range of literature to stay updated with the latest developments in their field, which is essential for maintaining the relevance and innovation of their work. recognizing the challenges and inefficiencies inherent in this process, considerable academic efforts have focused on ai-assisted scientific research @xcite . they aim to answer the researcher questions from both junior and senior researchers. these questions can be broadly classified into three categories: (1) scientific question answering @xcite @xcite , which seeks detailed information or clarification within a specific domain; (2) scientific text summarization @xcite @xcite @xcite , aimed at condensing the latest findings and developments into comprehensive overviews; and conversely, recent industry applications, like perplexity ai, foot_0 iask, foot_1 you.com, foot_2 phind, 4 and searchgpt, foot_4 allow users to inquire about anything beyond specific tasks. they use retrieval-augmented generation (rag) @xcite technique to offer an innovative integration of generative large language model (llm) with web search capability. the core idea behind them is to offer users not just any answer, but the most accurate and contextually relevant information available. however, the proprietary nature of industry applications has hindered their development and may impede academic research in this field. besides, both academic and industry applications serve as passive assistants, focusing solely on responding to user inquiries rather than engaging in active communication. to address these issues in academic and industry contexts, we developed openresearcher, an open-source project that harnesses ai to accelerate scientific research. its main workflow is shown in figure 1 , and its main contributions are as follows: ## openresearcher openresearcher is designed to leverage ai to speed up the research process by efficiently responding to researchers' inquiries. as shown in figure 1 , openresearcher employs rag to combine llms' internal knowledge with the latest external information. we design a data routing strategy for quick and precise information retrieval that can meet time and domain requirements. lastly, we have developed multiple tools, including query tools, retrieval tools, post-processing tools, generation tools, and refinement tools. openresearcher can flexibly use these tools to customize a workflow for each query. ## demonstration our web application is built with streamlit. 6 our databases encompass arxiv publications from jan. 2023 to jun. 2024, enriched with metadata. this is because most llms are trained on pre-2023 data, enabling them to retain this information. this fact also inspires openresearcher to answer simple questions without any retrieval, only using llms' internal knowledge. we utilize the state-of-the-art gte-large model @xcite as dense vector and efficient-splade-vi-bt-large @xcite as sparse vector to vectorize our queries and paper chunks. these vectors serve for hybrid retrieval, and we use qdrant foot_6 for the vector storage. this hybrid retrieval tool extracts the 30 most similar chunks from each selected database. elasticsearch foot_7 supports our implementation of bm25 retriever, which extracts up to 80 chunks. the bing foot_8 api finds 10 relevant outcomes for the internet retrieval tool. besides, we utilize bge-reranker-v2-m3 foot_9 to implement our reranking tool. this reranking tool reduces the number of retrieved chunks to 10. lastly, we use @xcite as our backbone llm to implement all llm-powered tools, while also supporting various online llm apis and locally deployed llms through ollama. 11 figure 2 , whose completed screenshot is shown in figure 3 of section a, demonstrates the strong capability of openresearcher. firstly, openresearcher can flexibly construct a tailored workflow for different queries, including simple queries and complex queries. for simple questions like "what is ppo?", it directly employs llms to produce answers. for more complex queries like "summarize the recent latest developments and variants of ppo?", it utilizes multiple tools and provides users with essential details, including active queries, rewritten query, decomposed sub-queries and their sub-answers, retrieved outcomes of each sub-query after post-processing, generated final answer, and citation. this example can showcase its flexibility in handling different queries. with this benefit, our openresearcher can speed up responses and reduce computational costs. secondly, this figure also shows openresearcher can pose questions to users for query clarification. different from previous passive applications that only answer questions, openresearcher utilizes llms' internal knowledge to help users specify their question details. this tool is very crucial for junior students who often struggle to clearly express their questions and confusion. thirdly, figure 2 demonstrates that openre-searcher supports conversational question answering, enabling users to engage in multi-turn dialogues. this feature allows for continuous and deeper discussions within openresearcher. lastly, this figure shows our openresearcher can enhance the quality and reliability of generated content by retrieving supporting evidence from the internet and arxiv corpus. additionally, we have developed a citation tool that links the generated text to the retrieved information, making it easy for researchers to verify the sources and delve deeper by reading the original papers. ## conclusion we introduce openresearcher, an active ai assistant to accelerate the research process, catering to a broad spectrum of inquiries from researchers. openresearcher employs retrieval-augmented generation (rag) to enhance llms with the latest, verified, and domain-specific knowledge. it interacts with users to clarify their queries. moreover, we have developed various tools for openresearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and refine these answers. openresearcher can use these tools flexibly to build a pipeline that delivers accurate and comprehensive answers, outperforming those from industry applications, as judged by human and gpt-4o.